sample resume for entry level data scientist

9 Entry Level Data Scientist Resume Examples for 2024

Landing your first job as an entry-level data scientist means presenting a resume that shows your skills clearly. This article guides you through crafting a resume that hiring managers will notice. Find examples and tips to highlight your data analysis, programming, and statistical skills. Learn how to organize your qualifications, projects, and education to start your data science career.

• 27 Mar 2024 - 1 new resume template (Machine Learning Engineer - FinTech Industry) added
• 19 Mar 2024 - 1 new section (Essential technical skills list) added
• 11 Mar 2024 - 1 new resume template (Junior Data Scientist - HealthTech Industry) added
• 03 Mar 2024 - 1 new section (Quantify your impact with numbers) added
• 24 Feb 2024 - 1 new section (Shape your resume for the job) added
• 16 Feb 2024 - 1 new section (Beat the resume screeners) added
• 08 Feb 2024 - 4 new sections, including 'Education placement on resume', added
• 08 Feb 2024 - 5 new resume templates, including Senior Data Analyst, added
• 06 Feb 2024 - Article published
• 03 Feb 2024 - Resumes approved
• 01 Feb 2024 - First draft complete

  Next update scheduled for 04 Apr 2024

Here's what we see in standout entry-level data scientist resumes.

Show Impact With Numbers : The best resumes show clear impact with accuracy improvements , time savings , cost reductions , and efficiency gains . These metrics help you show the value you can bring to the role.

Match Skills To The Job Description : Include skills you have that match the job description. Highlight tools or techniques such as Python , R , SQL , Machine Learning , and Data Visualization .

Current Industry Trends : Stay updated with trends such as automated data cleaning . Show that you're ready to adapt and apply these in your role.

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Entry Level Data Scientist Resume Sample

Education placement on resume.

As an incoming data scientist, it's crucial to leverage your educational experience. Place your educational background at the beginning of your resume. This showcases your strong theoretical knowledge base, particularly important for entry-level positions where you may lack extensive professional experience.

Include relevant coursework that might differentiate you, such as advanced statistics, machine learning, or data visualization. Also, highlight any thesis or significant projects related to data science. This will provide a practical context for your theoretical knowledge.

Displaying quantitative skills

An entry-level data scientist role requires a strong mathematical and statistical foundation. Ensure to include quantitative accomplishments in your resume. Did you manage to improve a process or identify a key insight during a school project? Quantify these achievements. It shows you can not only handle data, but also draw valuable conclusions.

Data scientists often require proficiency with specific tools such as Python, R, and SQL, or data visualization platforms like PowerBI or Tableau. Showcase your familiarity or proficiency with these tools prominently to attract the attention of hiring managers.

Keeping your resume concise

For entry-level data scientist positions, it's most effective to keep your resume to one page. It presents a clear and focused snapshot of your abilities, indicating your aptitude for the meticulous, precision driven role of a data scientist. It also shows respect for hiring managers’ time, who often sift through dozens, if not hundreds, of resumes.

If you find your resume stretching beyond one page, review your content ruthlessly. Prioritize recent and relevant educational and work experience, and cut older or less relevant information. It's about showcasing quality, not quantity, of your experience.

Data Science Associate Resume Sample

Showcasing problem-solving skills.

Problem-solving is at the heart of being a data scientist. Illustrate your knack for problem-solving by sharing instances where you've identified complex problems, dissected them, and formulated effective solutions. Even scenarios from academic projects or hackathons can illuminate your abilities.

Additionally, communication is key in data science, as findings often need to be translated to non-technical teams. Highlight any experience or activities where you had to present complicated information in a clear, understandable way. This shows you not only understand data, but can also help others understand it as well.

Entry Level Data Scientist with Data Visualization Specialization Resume Sample

Beat the resume screeners.

When you apply for an entry level data scientist role, your resume may first be read by a computer program known as an Applicant Tracking System (ATS). It's important to format your resume in a way that this system can read it well. This means avoiding images or graphics that contain important text and using standard section headings like 'Work Experience' and 'Education'.

Here are some key things to keep in mind to help your resume get past the ATS:

• Use relevant keywords from the job description. For example, include skills like 'data mining' or 'machine learning' if they match your abilities.
• Make sure your layout is simple. Use clear headings and bullet points to list your skills and experiences.

These steps will help ensure that your resume is ATS-friendly and that it will be seen by a hiring manager for the entry level data scientist positions you are applying for.

Marketing Associate to Entry Level Data Scientist Resume Sample

Shape your resume for the job.

You want to show you fit the data scientist role you're after. Think about what skills and experiences are most important for someone who works with data. Match your resume to these needs. This tells me, as a hiring manager, that you understand the job and are ready to step in.

• Put your most relevant data projects first. Use terms like data analysis , machine learning , and data visualization .
• Show how you solve problems. Mention a time you used statistical methods or predictive models to make decisions.
• If your past work was not in data science, show matching skills. For example, if you used Excel for data tracking, that’s useful.

Senior Data Analyst Resume Sample

Essential technical skills list.

If you're stepping into the data science field, your resume needs to show your technical prowess. Focus on the skills that will make you stand out as an entry-level data scientist. Here's a list of skills you might include, depending on the job you want:

• Python or R for data analysis and modeling
• SQL for database management
• Machine Learning techniques
• Data Visualization tools like Tableau or PowerBI
• Big Data platforms such as Hadoop or Spark
• Statistical analysis abilities
• Data wrangling skills with Pandas or NumPy
• Git for version control

You don't need to list every skill, just those that match the data science role you're aiming for. For example, if the job focuses on data visualization, emphasize your experience with Tableau or PowerBI . Place these skills in a dedicated section on your resume to help it pass Applicant Tracking Systems (ATS), which employers use to filter candidates.

Remember, show how you've used these skills in real projects or during your education. This helps employers see your practical experience, which is valuable even at the entry level.

Junior Data Scientist - HealthTech Industry Resume Sample

Quantify your impact with numbers.

When you apply for a data science role, you must show the value you can bring. Use numbers to make your impact clear. Numbers help hiring managers see your potential quickly and easily.

Think about your past work or projects. Look for ways you have used data to create value. Here are some ideas:

• How you improved a process - maybe you made a model that increased efficiency by 20% .
• If you worked on a project, did it help make decisions faster? Maybe you cut down the time needed to analyze data by 30% .

Remember, even if you're not sure about exact numbers, you can estimate. Think about how your work changed things. Did it make a system run faster or help save money? For instance, if you created a predictive model, estimate how much it could increase accuracy or reduce errors. Consider metrics like:

• Accuracy increase by 15%
• Error reduction by 25%
• Cost savings of $10,000
• Time savings of 5 hours per week
• Boost in data processing speed by 40%
• Reduction in customer support issues by 50%
• Upgraded 3 major data systems
• Conducted over 100 hours of data analysis leading to actionable insights

Machine Learning Engineer - FinTech Industry Resume Sample

•   Senior Data Scientist Resume Examples
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•   SQL Developer Resume Examples
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With advancement in technology over the years, it has become almost impossible to escape data. As per the US Bureau of Labor Statistics reports, data scientists are projected to grow by 31% .

To bag a decent job in the current market, you need to create a recruiter-friendly resume following updated industry standards. Lucky for you, this guide will provide you a well-rounded source for all those tips

Here are the entry level data scientist resume tips to create the best entry level data scientist resume:

• What is the suitable resume format for entry level data science resume?
• How to organize your entry level data scientist resume with distinct sections?
• How to optimize the header section of your entry level data scientist resume for maximum impact?
• How to write a perfect professional experience section of the data science resume?
• What is the best way to enter educational details in your entry level data scientist resume?
• What are the data science skills you should include?
• How to curate an impeccable entry level data scientist resume summary?

Data Scientist Salary

The salary of a data scientist depends on various factors including place of work, seniority level, location, and so on. However, you can get higher pay by developing your skills and being updated with industry trends.

Here is a list of data scientist salaries based on cities:

Entry Level Data Science Resume Formats

Your junior data science resume may get rejected despite being up to date. It could be because it did not get past the ATS, which most companies have as of late. Along with including all of your professional details, you must ensure that your data science resume is ATS-friendly.

To hold together your details together, you need to pick the most suitable resume format.

Also read : How do I make sure my resume is ATS-compliant?

Chronological Format:

This format has a time based approach, highlighting most recent experience first. It gives a clear insight into your career trajectory.

Along with being recruiter-friendly, it is also ATS-friendly because of it's error-free structure. It also shows that the candidate has nothing to hide, so if you have gaps in your resume, you might want to skip this one.

Functional Format

It is used by professionals who changed their industries and by those who have gaps in their professional experience timeline. However, it has the con of being non-ATS-compliant.

Combination Format:

You can opt for resume format if you have vast experience in your field of work. This format allows you to broadly highlight your skills as well as describe your work experience

Also read: How and when to use the correct resume format?

Organize Your Entry Level Data Science Resume

An ideal resume should contain all the distinct sections that can make the resume ATS-compliant as well as recruiters-friendly.

The following is a list of the traditional resume sections:

• Personal Information
• Profile Title
• Summary/Objective
• Professional Experience

You can provide the details of the following to further explore your professional and academic achievements:

• Certifications (if any)
• Awards & Recognition (if any)
• Additional Information (if any)

Also read: How to divide a resume into different sections?

Optimize Your Data Science Resume Header

The topmost section of your data science resume is an ideal segment to label your resume and provide your personal information.

Entry Level Data Scientist Resume: Header

Your junior data science resume needs to be labeled with your name to distinguish yourself from the other applicants.

Follow the given tips to frame a flawless resume header:

• Write your resume header in the largest font size of 16-20 points .
• Leave a single space between your first name and last name.
• If you have a middle name, write only the initial of your middle name followed by a period.

Entry level data scientist resume sample for header:

Entry Level Data Scientist Resume: Personal Information

You should provide the following details in an ideal personal information section of your data science resume:

Updated Contact Number

Professional email address, current location.

• GitHub/Kaggle link

While giving out the contact number you need to make sure that it is correct and active so that the recruiters can easily reach you.

Some recruiters may want to interview you over the phone or simply call you up to get a confirmation for any face to face meetings.

Tips to write the contact number on data science entry level resume:

• Always mention your personal number, not your parent's or someone else's.
• Write the country ISD code in front of your contact number and use a plus(+) sign before the ISD code

Avoid giving out any email address that has a made-up name because it is not considered professional. Your email address should always have your real name.

• johndoe27@gmail[dot]com
• john.doe@gmail[dot]com
• doe.john03@gmail[dot]com
• iamj0hn3283@gmail[dot]com

If you are considering a job in some other country other than yours then you can mention your location as city, country code

Else simply mention your location as city, state code.

There is no need for you to mention your personal home address in your entry level data science resume template so make sure that you avoid unnecessary details like your house number, street name, etc.

Social Platform Links

As an entry level data scientist, the candidate must have experience in conducting academic projects. Such projects give a glimpse of the candidate's skills to the hiring manager through GitHub or Kaggle.

Aside from that, you can also include your LinkedIn profile, if it is updated and active. It will help recruiters explore your candidacy and make sure if you are the right fit for the job.

A lot of recruiters prefer candidates who give their LinkedIn profile because it allows a lot of room to review their application, more than other applicants.

Data science resume sample for contact information section:

Also read: How to compose a crisp contact information section?

Entry Level Data Scientist Resume: Profile Title

Your profile title is the representation of your professional status in a resume and needs to be accurate at all times.

The profile title in your data science resume can communicate the following facts to the recruiters:

• Your current designation.
• Your functional industry.
• Your level of professional seniority.

Here is what you need to do while framing your profile title in a resume for data scientist:

• It should be the second-largest text in the resume after your resume header.
• It should ideally be framed in the 14-16 font size.

Here is a snapshot of an ideal profile title from our entry level data scientist resume template:

Perfect Your Entry Level Data Scientist Resume Professional Experience

The professional experience section is one of the most important sections of your data science resume. Since a lot of companies use the ATS, you must make sure that your resume writing standards are updated and recruiter-friendly.

Here are some tips you can follow:

Framing Points:

• You need to break down your work record in one-liner entry level data scientist resume points
• Start your points with a power verb to discuss your work history
• Validate your achievements by mentioning achievement figures

Grouping & Highlighting:

• Create different headings and list all the similar points under relevant subheadings
• Select words or phrases that throws light into your productivity and bold them

Doing so can make the recruiters acknowledge your professional involvement in executing the roles and responsibilities assigned to you.

Also read: How to craft a job-winning professional experience section?

Look at the ideal entry level data scientist resume example of the professional experience section presented through our data science resume sample given below:

Include Your Educational Details in Your Entry Level Data Scientist Resume

A data science resume is incomplete without the education section.

To be a Data Scientist you need to have a Bachelor's degree in Computer science, Social sciences, Physical sciences, and any relevant field of study. If you have more than a Bachelor's degree make sure to mention the same.

Here is a list of the details that you need to provide:

• The name of the school/university
• The location of your school/university.
• Joining and graduation dates in the mm/yy format
• Relevant course modules

There is an advantage of providing your educational details, especially if you are writing an entry level data scientist resume. Your lack of experience can be covered with the educational qualification that you hold.

To further help you get a clear picture of an ideal education section of a resume, here is a snapshot of our entry level data scientist resume sample:

Entry Level Data Scientist Resume Certifications

Get the attention of the recruiters by providing the details of your certification(s).

If you are a certified data scientist, it can have a positive impact on your job application and make the recruiters give you extra credit.

The certifications section of your data science resume can help you communicate the following details about you to the recruiters:

• Certification course name.
• Name of the institute of affiliation.
• Completion date of the course in the mm/yy format.

Here are a couple of entry level data science certifications:

• Cloudera Certified Associate (CCA) Data Analyst
• Cloudera Certified Professional (CCP) Data Engineer
• Data Science Council of America (DASCA) Principle Data Scientist (PDS)
• Dell EMC Data Science Track (EMCDS)
• IBM Data Science Professional Certificate
• Microsoft Certified: Azure Data Scientist Associate
• Open Certified Data Scientist (Open CDS)
• SAS Certified Data Scientist

Here is an entry level data science resume example showcasing the ideal certifications section for your data scientist entry level resume:

Entry Level Data Science Resume: Additional Information

There are various details that you can mention in your junior data scientist resume.

For example, if you have the ability to speak or write more than one language you should mention the same in your junior data scientist resume.

Doing so can make you stand out as an applicant who has the ability to communicate with others who speaks a different language.

Apart from these, you can always provide the details of any extracurricular activities that you have been involved in to emphasize your experience in creative learning, especially as an entry-level applicant.

Such extra details will help you in communicating your soft skills effectively and be considered as an active student and professional. However, it would be a waste of space if you are an experienced professional, because at that point recruiters care more about hard skills.

Entry Level Data Science Projects for Resume

Including your academic projects in your data science resume is a good way to attract recruiters' attention. It helps the recruiters gauge your skills and work experience which will help you stand out from the crowd.

Here are the steps to write a perfect data science projects for resume section:

• Give a short project description
• Mention the tech stack of the project
• List your roles and responsibilities along with some of the accomplishments of the projects with figures

EXAMPLE : Project: Readme Analysis | Tech Stack: Tableau, Scikit-learn, SQL

• Deployed NLP to understand the programming language used in the repo with 89% accuracy
• Employed an API to transcode the programming language into a read me file for each report

Present Your Entry Level Data Scientist Resume Skills

The skills section is another important segment of your data science skills resume wherein you can inject suitable keywords that can help your data science resume get past the ATS.

Go through the professional experience section of your best entry level data scientist resume and pick the skills that elucidate your professional caliber.

Avoid cramping up the skills section with phrases as it may affect the effectiveness of your data science skills resume.

The whole point is to make the skills section crisp and make the recruiters recognize your potential as a data scientist.

Another thing you need to do while farming your skills in your junior data science resume is to create separate sections to highlight your technical and functional skills.

Here are some of the data science resume key skills:

Technical Skills:

Here is a data science resume example to help you see what an ideal skills section should look like in a data science resume:

Curate an Impeccable Overview of Your Entry Level Data Scientist Resume

What goes in the first half of your resume depends a lot on your level of experience. You can either frame a resume summary if you have enough years of experience, or draft a resume objective if you are an entry-level professional.

Entry Level Data Scientist Resume Summary

A suitable resume summary for entry level data scientist should include some of the distinct professional experience that you have acquired over the years and also highlight your core skills.

Here is a list of all that you should do to compose your resume summary for entry level data scientist:

• Write your summary at the end of resume-writing process as you will have a well-rounded mental overview of your work history.
• Pick the highlights of your career from the work experience section of your resume.
• Unless you have 10+ years of extensive work experience try not to exceed your resume summary to more than 3-4 lines.
• Start your sentences with power verbs and make sure that you maintain a cause-effect methodology.

Refer to the data scientist resume example for an ideal entry level data scientist resume summary:

Entry Level Data Science Resume Objective

Give an overview of your data science entry level resume by composing a resume objective. You should include an entry level data science resume objective if:

• You do not have any work experience.
• You have less or limited work experience below 3 years.
• You are a fresh graduate writing an entry level data scientist resume.

Instead of asking what the organization can provide you, rather examine what you can contribute to the organization, which is what you should include in an objective.

The ideal resume objective should be able to communicate your willingness to learn from the roles and responsibilities that would be given to you, along with highlighting your skills as a professional data scientist.

Entry Level Data Scientist Sample Resume

Before we go ahead with more about resume-writing, here is our complete data science resume template to help you understand what an ideal resume should look like:

• Software & Website: SAS, Google Cloud Analytics
• Programming Language: Python, R, C, C++
• Enhancing the advanced data analytics for supporting all go-to-market-strategies as part of a 60 member data science team
• Leveraging expertise in data-driven science in B2B commerce to increase intelligence in the go-to-market functionality
• Employing mathematics, statistics, econometrics, and operations research for developing machine learning solutions
• Contributing to diverse fundamental science research programs for studying human health problems like cancer & infections
• Executing independent data science projects with 20 customers for solving specific business problems
• Administering data science projects for configuring the solutions to maximize value for the customers
• Exploring and validating new techniques to incorporate into price segmentation
• Participating in the development, validation, and delivery of reporting tools
• Converting opportunities into product modules that are valuable across multiple industries
• Conducting technical research while leading and mentoring a team of 10 senior data analysts for business development
• Rendered assistance in translating requirements into technical specifications for data and reporting teams
• Translated requirements into design solutions for 20 business leaders , stakeholders, product managers, and internal teams
• Leveraged quantitative skills extensively to clean, transform & interpret raw data for providing data-driven recommendations
• Extracted, processed, and analyzed large data to solve the most pressing analytical issues
• Performed statistical analysis to identify internal performance pattern & devised data-driven strategies to optimize the same
• Improved business by 60% by conducting data analysis and executing projects for driving business recommendations
• Designed, implemented, analyzed, and tested 4 new features to improve the product suite
• Developed 7 end-to-end business intelligence solutions to advance Power BI functionality and features like Power BI server
• Crafted and delivered 6 performance monitoring dashboards to track business performance
• Conceived, built, launched, and maintained 4 dashboards to improve AV operations
• Liaised with 10 colleagues from sales, operations, product and finance teams to deliver solutions for improving operations
• Built 2 reporting and metrics tracking solutions to optimize cash management
• Conceptualized frameworks and quantitative models to seize new business ventures
• Developed 6 dashboards and frameworks to monitor business performance while creating business cases
• Drafted monthly reports on sales performance including operations & profitability to identify opportunities for improvement
• Developed 60 reports on client usage and performance prior to contract renewals to suggest best products for client scenarios
• Formulated and managed 10 execution plans of business intent to monitor results
• Built strategies and improved the profitability of the network by 10% while solving complex business problems
• Interpreted learnings into data pull and visualization for automation while creating metadata specifications for compliance
• Construed business requirements into proposed data definitions for creating 10 policies as per industry standards
• Certified Analytics Professional | Marble Academy | Berkeley, CA | Jul '20 - Dec '20
• Cloudera Certified Associate : Data Analyst | Cloudera | Jan '18
• Languages : English (Native) and Vietnamese (Interactive)

Key Takeaways

Here are some key takeaways from the guide to help you write a job-winning data science resume:

• Always make it a point to draft your data science internship resume in a suitable resume format.
• Label your resume with your name and provide your personal information in line with the hiring guidelines.
• One-liner points have a higher chance than paragraphs to be read by the recruiters and comprehend your work experience statements.
• Provide achievement figures to give the recruiters an idea of what you are capable of contributing to an organization's higher goals.
• Give an overview of your resume by including a suitable data science resume summary .
• Include data science projects to enhance your candidacy and show your expertise.

Go to Hiration's Online Resume Builder and create a professional resume for yourself. Additionally, reach out to us at [email protected] or get 24/7 professional assistance for all your job & career-related queries with our chat support.

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17 Data Scientist Resume Examples for 2024

• Data Scientist Resume
• Data Scientist Resumes by Experience
• Data Scientist Resumes by Role

Writing Your Data Scientist Resume

We’ve reviewed countless data scientist resumes and have made a concerted effort to distill what works and what doesn’t about each of them.

Our number one tip to create an effective data science resume is to quantify your impact on the business ! These 17 data scientist resume samples below and our  data scientist cover letter templates  can help you build a great job application in 2024, no matter your career stage.

Whether you’re looking for your first job as an entry-level data scientist or are a veteran with 10+ years of expertise, you’ll find plenty of tools to build your perfect resume, like our new  Word resume examples  or  free Google Docs resume templates .

Data Scientist Resume Example

or download as PDF

Why this resume works

• You need to  write your resume  in a way that  shows the employer that you’ve materially impacted the companies you’ve worked for.
• This means you should quantify your value in terms of business impact, not model performance. Model performance metrics without context really don’t convey much.
• They’re a way to quickly display your achievements and convince the employer that you’ll bring that same kind of energy to their team or company.

Entry-Level Data Scientist Resume

• Considering adding projects to your  entry-level data scientist resume  in lieu of enough work experience?
• You can demo the punch of a project by framing a question and then answering that question with data.
• Again, your results should be consistently expressed in numbers. Even if the result is as silly as saving 12 minutes per movie, it recognizes the importance of measuring impact.
• Customizing looks like: mentioning the target business by name and including relevant keywords from the  job description . 

Associate Data Scientist Resume

• When you have little to no professional background,  the skills you list on your resume  matter more than ever. And your abilities aren’t just selling points—they’re also a springboard for you to demonstrate your willingness to learn. 
• While writing your associate data scientist resume objective, immediately dive into any education or internship highlights with notable companies like Northrop Grumman. Then, sprinkle in some personality that shows your enthusiasm for new knowledge—drive and inquisitiveness are highly desirable traits in new professionals.

Senior Data Scientist Resume

• Your  senior data scientist resume  can really wow when you show a clear career progression from data analyst to data scientist to senior data scientist.
• That said, if you’ve got at least four years of experience under your belt, it’s fine for your work experience to account for about 70 percent of the page.
• A worthwhile summary should give a quick snapshot of your career highlights in two to three power-packed sentences and include the target company by name.

Data Scientist Intern Resume

• Call attention to your expertise in computer science by listing your proficiency in advanced programs like Keras on your data scientist intern resume.

Data Visualization Resume

• Whether it’s geospatial analysis, real-time data monitoring, or even creating standard visuals, make sure to quantify the impact of each and clearly state the benefit these tasks brought to the company to strengthen your data visualization resume.

Healthcare Data Scientist Resume

• Having two qualifications! Now’s the time to show all the degrees you’ve got! The best-case scenario is to have two degrees where one caters to the healthcare field while the other highlights your expertise in data science!

Amazon Data Science Resume

• Let that statement capture your aspirations and what you desire to bring to your new employer. Hiring managers are eager to see your passionate side and value to the team.

Python Data Scientist Resume

• Mentioning achievements such as improving project outcomes and reduction in process duration in your Python data scientist resume is a great way to leverage your experience honed over years of hard work.
• Then, by writing a great cover letter , you give yourself room to expound on exactly how you reduced process duration as a Python data scientist.

Data Scientist Machine Learning Resume

• Even if you already have ample experience in your field, you can give your data scientist machine learning resume a competitive edge by bringing your higher education to light. Create space to showcase your advanced degree in a relevant subject like statistics to further stand out.

Data Science Manager Resume

• Again, the results of your work should be stated clearly in terms of tangible impact (are you sensing a theme?). 
• Using a two-column layout for your  data science manager resume  allows more information to fit on a single page. Even with nine-plus years of experience, keeping your resume to one page is ideal.
• Fretting these details? Our  resume templates for 2024  may suit your specific needs; additionally, we’ve got 10 fresh and  free Google Docs resume templates  that can make your  resume-building  blues go away!.

NLP Data Scientist Resume

• When you’re trying to figure out  what to put on your resume  for a more specialized role like an NLP data scientist, it’s important you showcase your proficiency in operationalizing models to have a big impact on the business.
• Don’t focus on the technical aspects of the models you’ve built on your  NLP data scientist resume  (you’ll talk more about that in your interviews). Instead, take a step back and talk about the broad impact you’ve had in your previous roles.

Metadata Scientist Resume

• Prove your experience in programming, testing, modeling, and data visualization through well-designed projects that solve real problems through code.
• The key isn’t to reinvent the wheel but to create something dynamic and unique that isn’t easily replicated with a few Google searches and a video tutorial.
• Solve this problem with projects. If you’ve worked on excellent projects that used and showcased the necessary skills required for the job, list them and watch your resume bloom with confidence!

Educational Data Scientist Resume

• Think “well-rounded” as you write; you might include an exciting publication related to the job role, quickly outline your relevant experience or abilities, and conclude with how and why you’ll better the company through your new role. 
• Skills and certifications add credibility, but potential employers also want to know about your impact.
• If you performed evaluations, what improvements did you make afterward? If you integrated machine learning, what optimizations did you use it for?

Data Analytics Scientist Resume

• Your data scientist, analytics resume should target the list of requirements that companies in your state commonly request.
• For example, 18 out of 20  job descriptions  for data science, analytics in the state of California list Python, SQL, R, Tableau, and Hadoop (in that order) as required skills.
• After you add job-market-specific data, our  free resume checker  can assess your resume for other key elements like spelling, grammar, and active language. 

Data Science Consultant Resume

• To best represent your capabilities, use metrics to talk about your accomplishments.

Data Science Director Resume

• For an effective data science director resume, use a clean and simple resume template and format your work experience in reverse-chronological order. Doing so will put your most recent and relevant accomplishments at the top, making it the first thing a recruiter will look at.

Related resume guides

• Data Analyst
• Data Engineer
• Computer Science

Recruiters only spend an  average of seven-plus seconds reviewing your resume , so it’s vitally important that you catch their attention in that time. Our guide for 2024 takes you section by section through your resume to ensure you get that first interview.

You can successfully choose a winning  resume format in 2024  that will snag an employer’s attention.

Short on time? Here are the quick-hit summaries of each section you can apply to your resume:

• Whether for a company or yourself, what you’ve worked on should be the focus of your resume. Always try to include a measurable impact of your work.
• Make this the job title you’re looking for (e.g., “data scientist”), and don’t worry about a summary unless you’re making a career change.
• Only include technical skills that you’d be comfortable having to code with/in during an interview. Avoid a laundry list of different skills.
• Include relevant courses if you’re looking for an entry-level role. Otherwise, make your work the focus of your resume. If you went to a boot camp, list it here.
• Double-check everything. This is not the place you want to make a mistake. You don’t need to put your exact address. City, state, and zip are fine.
• Try to keep it to one page. Keep your bullets brief. Triple-check your grammar and spelling, and then have someone else read it.
• Read the  data scientist job description . See if any projects you’ve worked on come to mind while reading it. Incorporate those specific projects into your resume.

Your data science projects and work experience

Let’s jump right into the good stuff and talk about the most important part of your resume: your work experience and projects. This is it. This is the grand finale. This is where the person reviewing your resume decides whether or not you’ll get an interview.

When talking about your previous work (whether that’s for another employer or on a side project), your goal is to convince the person reviewing your resume that you’ll provide value to their company. This is not the place to be humble. We want to see that “I’m wearing my favorite outfit” level of confidence.

The template for successfully talking about your experience as a data scientist is:

• Clearly state the goal of the project
• You can mention the programming languages you used, the libraries, modeling techniques, data sources, etc.
• State the quantitative results of your project

You’re a data scientist, so highlight your value by demonstrating the quantitative impact of your work.  These can be estimates . For example, did you automate a report? Roughly how many hours of manual work did you save each month? Here are some ideas for how you can quantitatively talk about your projects:

Ways to define the impact of your data science work

• Example:  You developed a pricing algorithm that resulted in a $200k lift in annual revenue.
• Example:  You built a model to predict who would cancel their subscription and introduced an intervention to improve monthly retention from 90% to 93%.
• Example:  You built a marketing attribution model that helped the company focus on marketing channels that were working, resulting in 2,100 more users.
• Example:  You ran an experiment across different product features, which resulted in a 25% increase in engagement rate.
• Example:  As a side project, you built a movie recommendation engine that now saves you 26 minutes each time you need to decide which movie to watch.
• Example:  Since you built a customer segmentation model to determine how to communicate with different customer types, customer satisfaction is up 17%.

Numbers draw attention, are convincing, and make your resume more readable. Which of these two ways to describe reporting is more compelling?

• Used Python, SQL, and Tableau to conduct daily reporting for the business
• Using Python, SQL, and Tableau, combined 11 data sources into a comprehensive, real-time report that saved 10 hours of work weekly

If nothing else, please take this away from this guide:  state the results of your projects on your resume in numbers.

Trade-offs between projects and work experience

Simply put, the more work experience you have, the less space “projects” should take up as a section on your resume. In the sample resumes above, you’ll notice that only the more entry-level data scientist resumes have a section for projects.

The senior-level resumes focus on projects in the context of experience within companies. Real estate is precious on a one-page resume, so you’ll want to focus on the bullets that most clearly demonstrate how you’re a great fit for the job. Companies want to hire data scientists who have demonstrated success at other companies.

Entry-level data science projects for resume

Junior data scientists should include projects on their resumes. Try starting with a  resume outline , where you can brain dump anything and everything about your projects; then, you can distill the best of it into your final resume. Can you share the Github link? Do you have a link to a write-up you did about your project?

The more initiative you can show for entry-level data science projects, the better. Do you have any questions to which you’ve always wanted the answer? You can probably think of some clever ways to get data around that question and come up with a reasonable answer. For example, our co-founder wanted to know  which data science job boards were best , so he pulled together some data, laid out his assumptions and methodology, and made his conclusions.

No matter what projects you include on your resume, be sure to clearly state the question you were answering, the tools and technologies you used, the data you used to answer the question, and the quantitative outcome of the project. Succinctly stating conclusions and recommendations from your analysis is a highly sought-after skill by employers in data science.

The data scientist summary

Since you have limited space on your resume, you should only include a  resume objective  if you take the time to customize it for each role to which you apply.

You may want to include a  resume summary  or objective when you’re making a big career change. If you do include one, make sure to keep it specific about your goal and experience. This is valuable space you’re going to be using on this statement, so take the time to personalize it to each job.

Include the title of the job you’re looking for under your name. This should be aspirational. So if you’re a data analyst looking to apply for data scientist jobs, you would put “data scientist” under your name as the headline:

Skills that pay the bills

The most common mistake we see on data science resumes (that we used to make on our resumes) is what we call skill vomit. It’s a laundry list of skills in which no one person could have expertise. A quick rule of thumb:  if the skills section takes up a third of the page, it takes too much space. This is a big red flag for hiring managers.

The reason people make such an exhaustive skills section is to get through the mythical data science resume keyword filters. If you’re changing your resume in small ways for each job you apply to (for example, put Python for jobs that mention Python and R for jobs that list R if you know both), you’ll have no problem with those keyword filters.

The rule of thumb that we recommend you use in determining whether to include a skill on your resume is this:  i f it’s on your resume, you should be comfortable coding with/in it during an interview.

So that means if you’ve read a few articles on Spark or adversarial learning, but you can’t use them in code, they should not be on your resume. If you only have a handful of tools under your toolbelt, but you can use them effectively to answer questions with data, you’ll be able to find jobs looking for that skill set. 

We can assure you there are all kinds of data science jobs available. Our scraper that indexes jobs across thousands of company websites shows over 5,000+ full-time data science job openings in the US across all tenures and skill sets. And our scraper has a lot of room for improvement, so that’s significantly lower than the actual number. 

There are tons of fish in the job market sea; you just need a fishing rod.

Entry-level vs. senior skills sections

Generally, the more senior you are, the shorter your skills section needs to be. If you’re a senior data scientist, you should talk about the major tools and languages you use but save specific modeling techniques for the “Work Experience” section. Show how you used particular models in the context of your work.

When you’re more junior, you likely haven’t had the chance to use all of the techniques you’re comfortable with within work or a project. That’s okay! It’s expected. But you still want to make it clear to a potential employer that you can use those methods or libraries.

Education is a lot like skills in that the more senior you are as a data scientist, the less space the education section should take up on your resume. When you’re looking for one of your first data science jobs, you might want to include courses relative to data science to demonstrate you have a strong foundation.

Classes in subjects like linear algebra, calculus, probability, and statistics and any programming classes are directly relevant to being a data scientist. If you’re looking for your first job out of college, you should include your GPA on your resume. When you have a few years of work experience, it’s not necessary to include it.

If you just finished (or are finishing) a data science boot camp, this is the place to list where you went. You can include the relevant lessons or classes you took. Be sure to have a few projects from your boot camp (especially if it was an original project) in your resume’s “Projects” section.

Contact information

The takeaway from this section is simple:  this is not where you should make a mistake . Storytime! When our co-founder was first applying to jobs out of college, he realized about 20 applications in, he had spelled his name “Stepen” instead of “Stephen.” Don’t pull a Stepen.

Data suggests that when your email is wrong, your response rate from companies drops to zero percent. That’s just math. We’ve seen exactly four data science resumes where the email address on the resume was incorrect.

Make sure your email address is appropriate. While we don’t doubt the authenticity of your “ [email protected] ” email, maybe don’t use it when applying for jobs. To play it safe, stick to a combination of your name and numbers for your email.

This is the section you can include anything you want to show off for a data science role. Have a blog where you document the analysis you do for Dungeons & Dragons? Active on Github or an open-source project? Include a link to anything relevant to data that will help you stand out in your application.

General resume formatting tips

This section is just a list of one-off styling and formatting tips for your data science resume:

• Keep it brief. Bullets should be informative but should not drag on for paragraphs.
• Each bullet point in your resume should be a complete thought. You don’t have to have periods at the end of each bullet.
• Keep your tense consistent. If you’re referring to old projects in the past tense, do that for all old projects.
• Please, please don’t get your contact information wrong.
• Don’t give the person reviewing your resume a silly reason to put it in the “No” pile.  Check your resume  carefully.

Customization for each application

You don’t have to go overboard with your resume customization. Here are the steps we recommend to customize it for each job:

• So in this example, we’ll have one “Python” resume and one “R” resume depending on what the job is seeking.
• For example, if you have experience with attribution modeling and this is a marketing data science role, you should include that experience.
• Do you have experience with a certain library or modeling technique they mention? 
• Do you have experience in the domain of the specific job?
• Do you have any relevant industry experience with the company?

Let’s walk through a specific example to highlight what we mean by including particular projects for different jobs. Let’s say that a senior data scientist is applying for the position below.

In the “Ideally, you’d have” section, they mention they want someone who has “Experience with ETL tools.” Let’s say that in reality, the candidate had a large role in building out data pipelines in his fictional role as a senior data scientist at EdTech Company.

So all we’d do is change that section of his experience at EdTech Company to talk about that project, as you see below:

Data science resume customization example

Original bullet on the resume: Worked closely with the product team to build a production recommendation engine in Python that improved the average length on the page for users and resulted in $325k in incremental annual revenue

Customized for the role: Built out our company’s ETL pipeline with Airflow, which scaled to handle millions of concurrent users with robust alerting/ monitoring

Customization for startups

For early-stage startups (anything less than 50 employees), one of the most important qualities they’re looking for in a hire is ownership. That means they want someone who can ask a question and come up with an answer with minimal instruction. 

If you want to stand out to these companies, you should demonstrate ownership in the way you list projects on your resume. Include active words like “drove” or “built” instead of passive language like “worked on” or “collaborated on.” We know this seems nit-picky, but this matters to early-stage companies. Hiring managers at companies this size are strained for time and will use any signal to weed people out.

Concluding thoughts

There you have it—a compelling, easy-to-read data science resume built for 2024. Now you can celebrate by doing something as fun as  writing a resume . Maybe your taxes? Or go to the dentist?

By building or  updating your current resume , you took a huge step toward landing your next (or first) data science job. Now please, we beg you, check your grammar and spelling again and have someone else read your resume. Don’t let that be the reason you don’t get an interview.

Congrats! The first and hardest step is done. You have a data science resume! With great power comes great responsibility, so go and apply wisely.

Land your next job with our AI-powered, user-friendly tool.

Gut the guesswork in your job hunt. Upload your existing resume to check your score and make improvements. Build a resume with one of our eye-catching, recruiter-friendly templates.

• Work in real-time with immediate feedback and tips from our AI-powered experience. • Leverage thousands of pre-written, job-specific bullet points. • Edit your resume in-line like a Google Doc or let us walk you through each section at a time. • Enjoy peace of mind with our money-back guarantee and 5-star customer support.

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12 Data Scientist Resume Examples - Here's What Works In 2024

Data scientists are one of the hottest jobs of 2023. however, it’s also one of the most analytical, results-driven, and requires superb use of numbers. if you can show that on your resume, you’ll be on your way to a nice career as a data scientist. here are five data scientist resume templates to help you get an idea of what to put in your resume..

If career growth is one of your main qualifications for your next job, a career in data science is perfect for you. According to Towards Data Science , it’s the fastest-growing job on LinkedIn with an estimated over 11 million jobs by 2026. And it deserves to have such a bright future. You can apply for this job in several industries like e-commerce, IT, business, and much more. Because this field is so versatile, you can apply your skills somewhere that would greatly benefit others, not just a company. For example in healthcare, you can help visualize and manage data necessary for operation procedures. For a job like this, you need to be good with numbers and data. The ability to use statistics, analyze complex data, simplify it, and present it more easily for others are all necessary components of the job. You’ll need to display these skills, plus some experience with computer programs like Amazon Web Services to handle big data, in your resume. Today, we’ll be sharing with you the tips you need to make a data scientist resume that recruiters will look at.

Data Scientist Resume Templates

Jump to a template:

• Data Scientist
• Senior Data Scientist
• Entry Level Data Scientist
• Data Science Manager
• Data Science Vice President
• Junior Data Scientist
• Career Change into Data Science

Jump to a resource:

• Keywords for Data Scientist Resumes

Data Scientist Resume Tips

• Action Verbs to Use
• Bullet Points on Data Scientist Resumes
• Frequently Asked Questions
• Related Data & Analytics Resumes

Get advice on each section of your resume:

Template 1 of 12: Data Scientist Resume Example

A data scientist uses and processes raw data to discover interesting insights that help organizations make more informed decisions. They are part of the entire life cycle of data science projects. This means they work on collecting and storing data, as well as in data processing, developing data models, data analysis, and visualization. Cloud migration is now an in-demand skill for data scientists, due to the rapid adaptation of cloud services. Hence, it might be a good idea to include cloud migration skills on your resume.

We're just getting the template ready for you, just a second left.

Tips to help you write your Data Scientist resume in 2024

   include up-to-date data analysis or big data skill sets on your resume, like tinyml..

Data science is a fast-changing field, and hiring managers particularly at tech companies or startups love when candidates include recent technologies. One example is TinyML or other ML algorithms. Machine learning algorithms are perfect for processing large sets of data, especially when working with cloud-based systems with unlimited bandwidth. It might be worth including a project on your resume where you used ML or insights from an ML algorithm to improve the bottom line at your company (if you drove revenue or saved costs as a result of running a data science algorithm, hiring managers will be thrilled).

   Indicate your proficiency in data visualization tools like Tableau or Google Charts.

Mention projects in which you used your data visualization skills to present your insights. Data visualization plays a huge role in data science projects, so it’s important to demonstrate you have experience in this area.

Skills you can include on your Data Scientist resume

Template 2 of 12: data scientist resume example.

Because you are working with data that provide to you or you provide other departments data to use, you need to display successful collaboration with results in your resume. This sample does this by talking about what company goals were accomplished with other teams using metrics to highlight the achievements.

   Numbers and metrics relevant to data scientists

You can see examples of metrics to go with the companies’ achievements. For example, this person increased “customer traffic by 75%”, and generated “$1 million in wealth management sales”. Data science is always aligned with company KPIs, so list your achievements in a way that describes how you solved a company’s problem.

   Strong action verbs related to data scientists

When you read this sample, you’ll see words like “implemented”, “optimize”, and “reduced.” All these are action verbs that communicate the ability to do/succeed in a task. Include strong action verbs in your resume that communicates your ability to organize projects and collaborate with others.

Template 3 of 12: Senior Data Scientist Resume Example

Senior data scientists outline project requirements, delegate tasks to junior data scientists, monitor their performance and carry out upper-level responsibilities. Their purpose is to drive companies to success by using data analytics. Your potential employer might expect you to have extensive experience in data science, so it’s important to demonstrate seniority on your resume. You should prioritize relevant job experience and highlight your leadership background.

Tips to help you write your Senior Data Scientist resume in 2024

   indicate your proficiency in r, python, or other relevant programming languages by mentioning previous projects in which you used them..

Since most companies are generating a large amount of data, you need specific programming languages such as R or Python to process them. That’s why your potential employer might be looking for an experienced senior data scientist in these programming languages.

   Demonstrate experience in formulating and overseeing data-centered projects.

A senior data scientist is a leadership role. You will be supervising other junior data scientists to ensure they follow certain standards and processes, whether that involves cleaning or exploration. That’s why it is important to demonstrate on your resume that you have experience with developing and monitoring these types of projects.

Skills you can include on your Senior Data Scientist resume

Template 4 of 12: senior data scientist resume example.

If you’re trying to climb up to the top of the data scientist ladder, you need to show that you excelled in lower positions. Don’t forget to list what you did that earned you an upper-level role in your previous job. Recruiters love to see that you desire to grow. Talking about your transitions is key in this kind of resume.

   Shows growth in promotions

In the sample, you see that there was a promotion within a short amount of time at a company. If you had a promotion, emphasize it by separating the job titles and explaining what work you’ve done that contributed to you getting promoted.

   Numbers and metrics relevant to senior data scientists

Don’t just list promotional achievements without also providing the metrics. Recruiters want to see how you’ve been beneficial to the previous company, and numbers are a great way to show your achievements. That gives recruiters an idea of how you can help their company out.

Template 5 of 12: Entry Level Data Scientist Resume Example

As an entry level data scientist, you'll be dipping your toes into the world of analyzing and interpreting complex data sets to help businesses make informed decisions. While the demand for data scientists has been booming in recent years, competition for entry-level roles can be fierce. To stand out, your resume should showcase your technical skills and demonstrate your ability to turn raw data into valuable insights for the company. Think about highlighting projects where you've used relevant programming languages, machine learning techniques, and data visualization tools. In addition to showcasing your technical expertise, don't forget to highlight any internships or relevant work experience you have related to data analysis. Companies are not just looking for technical wizards; they are also seeking individuals who can work well with others, translate complex findings into understandable insights, and ultimately drive business growth. Make sure to include any instances where you've collaborated with cross-functional teams or presented data-driven findings to non-technical stakeholders.

Tips to help you write your Entry Level Data Scientist resume in 2024

   show off your technical skills.

As an entry level data scientist, you should emphasize your programming abilities and proficiency in languages like Python, R, and SQL. Additionally, mention any experience working with data analysis tools, such as Tableau, to demonstrate your ability to visualize and communicate results effectively.

   Highlight your problem-solving capabilities

Data scientists need to be adept at solving complex problems and uncovering insights from raw data. Use your resume to share examples of how you've approached and solved data-related challenges, emphasizing your analytical mindset, creativity, and critical thinking skills.

Skills you can include on your Entry Level Data Scientist resume

Template 6 of 12: entry level data scientist resume example.

Right out of college, you may not have much experience in the field. To supplement that, use your experience in clubs and activities, class projects, and useful coursework to help highlight your knowledge on the subject. Internship experience is essential, as well; any numeric results or accomplishments should be acknowledged. This sample does so by listing the percentages of costs, labor, and hours reduced thanks to their work.

   Strong data scientist technical skills

Not only are key skills listed in the skills section (things like MATLAB or SQL), you can also see this sample mention the use of some of these skills throughout their experience. You should also include skills that are relevant to data science jobs that you have - review the job description that you're applying to for skills the job is looking for.

   University projects relevant to data scientists

Class projects are good examples of how a recent grad has applied critical job skills. In the descriptions, it also lists awards won. This shows that the projects they worked on were successful in applying what they learned to get results.

Template 7 of 12: Data Science Manager Resume Example

A data science manager has an administrative and technical role. They are responsible for guiding and overseeing the data science team. Hence, they will determine project outlines, deadlines, and priorities, and ensure team members follow specifications. As a data science manager, you should ideally have a master’s degree in data science or equivalent experience. You can take your resume to another level by demonstrating your impact on previous projects’ results. This way, you are showcasing your tangible value.

Tips to help you write your Data Science Manager resume in 2024

   include your data science certifications on your resume..

Your data science manager resume should highlight your academic value and expertise, and certification is a great way to demonstrate that. These are third-party validated credentials that exhibit your skills and years of experience.

   Highlight your project management skills through relevant work experience.

Data science managers should have project management skills to successfully drive success to the data science team. Recruiters are looking for past evidence of assigning tasks, prioritizing deliverables, providing feedback, conducting research, and ensuring team members’ performance. To highlight this, include action verbs like "Led" or "Managed".

Skills you can include on your Data Science Manager resume

Template 8 of 12: data science manager resume example.

To be a successful manager in any role, you need to have the experience of a manager. A focus on team management and leading a team to great results are examples you should list on your resume. Showing recruiters that you can lead a team or data science project that brings high-yield results is what will set your resume apart from other applicants. Data science is all about using data to drive decision-making and top-level KPIs, so make sure you add accomplishments to your resume that highlight how your work has affected your company’s bottom line.

   Emphasis on managerial skills

You can see in the experience section of this sample how they led a few projects. They discuss what was done, who they worked with, and how big a team they had. Follow a similar layout in your resume so recruiters can see that you can lead data science teams.

   Tailored to the data science industry

One way that you can get your resume past the filtering system, or ATS, is to use specific keywords that are found throughout the job description. In this sample, you see keywords like “training and peer-mentoring”, “data systems”, and “regression analysis.”

Template 9 of 12: Data Science Vice President Resume Example

A Data Science Vice President sits at the intersection of data analytics, business strategy, and leadership. In recent years, your role has evolved from pure data analysis to one where you're expected to guide an entire organization's data strategy. As companies increasingly rely on data-driven decision-making, you're not just crunching numbers but explaining their implications to non-technical executives. When crafting a resume for this role, remember companies are looking for a strategic thinker who can leverage data to drive business growth, not just a seasoned analyst. As the field becomes more competitive, hiring managers are expecting more than just top-notch technical skills. They want to see a track record of transforming raw data into actionable insights that drive business results. They're also looking for leaders who can build and guide high-performing data science teams. So, make sure your resume reflects these demands and trends.

Tips to help you write your Data Science Vice President resume in 2024

   highlight strategic leadership.

As a Data Science Vice President, you're expected to be a strategic leader. Highlight instances where you've used data to inform business strategy. Show how you've influenced decision-making at the executive level by translating complex data into digestible insights.

   Focus on Team Building and Management

This role isn't just about your expertise with data, but also your ability to lead a team. Detail your experience in building, leading, and mentoring data science teams. If you've overseen sizeable teams or managed across different locations, ensure that it shines on your resume.

Skills you can include on your Data Science Vice President resume

Template 10 of 12: data science vice president resume example.

Like any VP role, the position of vice president of data science needs strong managerial skills. Not only will you need to manage a team, but that team will also have to consist of managers. Your goal is to implement and execute company-wide goals that greatly benefit the company. This sample lists out the processes done while managing managers lower on the corporate ladder, to bring in an increase of profit or a decrease in costs (or increase in productivity).

In this sample, the positions listed are all higher than the ones listed below. That shows recruiters that you have the ambition to climb to the top. Additionally, with each upper management role, you see growth in the people they work with; they started with “hired 8 new candidates” and are now “worked closely with a cross-functional team.” Show your incline in managerial responsibilities in your resume.

   Focused on the vice president of data science role

In the upper management positions of this sample, you see how it talks about working with other department teams to deliver results that are often well over 40%. Positive metrics like this help show your abilities as a capable vice president.

Template 11 of 12: Junior Data Scientist Resume Example

Junior data scientists are just data scientists that have under five years of industry experience, or have recently made a career change into the field. The title is sometimes used interchangeably with the regular 'data scientist', so you can use this template whether or not you're a junior data scientist or have some experience in the field.

Tips to help you write your Junior Data Scientist resume in 2024

   numbers and metrics relevant to data scientists, and good use of skills relevant to data scientists..

You can see examples of metrics to go with the companies’ achievements. Plus, all the skills mentioned are very relevant to the data science and engineering field.

   Good use of space

The two-column in this data scientist resume template prioritizes the work experience sections, while maximizing the content into the resume. The resume does not look overcrowded and uses reasonable margins. Not all two column templates are ATS-compatible, but this one is when it is saved as PDF and passed through a resume screener.

Skills you can include on your Junior Data Scientist resume

Template 12 of 12: career change into data science resume example.

If you're trying to break into data science, but don't have formal data science experience yet, use a template like this one.

Tips to help you write your Career Change into Data Science resume in 2024

   stress transferrable skills from your previous experiences.

Even if you didn't do data science work in your previous professional roles, you have technical experience as well as leadership, teamwork and analytical skill sets.

   Use keywords and skills from the new industry on your career change resume

To get past the applicant tracking systems and resume screeners, it's important that you use the right keywords for your target job, which in this case is a data science position. Even though you might have sales or product marketing experience, use keywords that are specific to data science only - including things like SQL/database experience, ML/AI experience, and other data preparation tools and techniques.

Skills you can include on your Career Change into Data Science resume

We reached out to hiring managers and recruiters at top companies like Google, Amazon, and Microsoft to gather their best tips for creating a standout data scientist resume. Here's what they shared:

   Highlight your technical skills

Make sure to showcase your proficiency in the key technical skills required for data science roles, such as:

• Programming languages (Python, R, SQL)
• Machine learning frameworks (TensorFlow, PyTorch, scikit-learn)
• Data visualization tools (Tableau, PowerBI, Plotly)
• Big data technologies (Hadoop, Spark, Hive)

Don't just list the skills, but provide specific examples of how you've used them in projects or previous roles. Quantify your impact whenever possible, like 'Built machine learning models using Python and scikit-learn to improve customer churn prediction accuracy by 25%.'

   Showcase your projects and their impact

Hiring managers want to see evidence of your ability to apply data science techniques to real-world problems. Include 2-3 of your most impressive projects, highlighting:

• The business problem or question you were trying to solve
• The datasets and techniques you used (e.g., data cleaning, feature engineering, model selection)
• The results and impact of your work, quantified if possible (e.g., increased revenue, reduced costs, improved efficiency)

Even if the projects were part of coursework or personal learning, they can still effectively demonstrate your skills and problem-solving approach.

   Tailor your resume to the job description

Data science roles can vary significantly between companies and industries. Carefully review the job description for each position you apply to, and customize your resume accordingly.

Look for key skills, tools, and domain knowledge mentioned in the job requirements, and make sure to emphasize your relevant experience in those areas. For example, if the job heavily focuses on natural language processing (NLP), highlight any NLP projects or coursework you've completed.

   Provide context for your achievements

When describing your accomplishments, provide enough context to help the hiring manager understand the significance of your work. Instead of simply stating what you did, explain why it mattered to your team or organization.

• Developed a machine learning model to predict customer churn
• Developed a machine learning model to predict customer churn, enabling proactive retention efforts that reduced churn by 20% and saved the company $500K annually

By connecting your work to business outcomes, you demonstrate your ability to drive meaningful impact and think strategically.

   Show your communication and collaboration skills

Data scientists rarely work in isolation; they need to effectively communicate insights to stakeholders and collaborate with cross-functional teams. Highlight experiences that showcase these critical soft skills:

• Presenting findings to executive leadership
• Collaborating with engineers to deploy models in production
• Partnering with domain experts to define business problems and requirements

Worked closely with product and marketing teams to develop customer segmentation models, leading to personalized marketing campaigns that increased conversion rates by 30%.

By emphasizing your communication and collaboration abilities, you show that you can bridge the gap between technical and non-technical audiences.

   Demonstrate continuous learning and growth

The field of data science is constantly evolving, with new techniques and tools emerging regularly. Hiring managers want candidates who are committed to ongoing learning and staying up-to-date with industry trends.

Highlight any relevant coursework, certifications, or independent learning you've undertaken to expand your data science skills. This could include:

• Online courses (e.g., Coursera, edX, Udacity)
• Participation in data science competitions (e.g., Kaggle)
• Attendance at conferences or workshops
• Contributions to open-source projects

By showcasing your continuous learning efforts, you demonstrate your passion for the field and your ability to adapt to new challenges and technologies.

Data science is a broad job category. You could have a focus on designing machine learning algorithms/predictive analytics, or data visualization, or mathematics and statistics. You may even have more of a focus on the business side of things. No matter which area of data science you’re in, follow these tips to help you tailor the perfect resume.

   Think it all through first

Before you start filling out your resume, have a brainstorming session. What programs, teamwork-based, or other hard skills do you have that are relevant? What are some of the achievements you’ve had on the job? Did you do (and succeed) any data science projects? Have an idea of all of that first. Then, write it out in your experience. The key is to ensure you’re including quite a few metrics. A role that involves a lot of data requires someone who is good at handling big numbers and knows how to effectively use the info. If that data involves cooperation from another department, include that as well.

   Edit it so the resume is fitting for the job description

When you finish writing it, reread the job description. How well do you think you did in matching your resume’s keywords with the job opening’s keywords? Have you left out the filler information? (You should; only make space for what’s necessary, especially when you have lots of experience.)

  Include personal projects

For those of you who are transitioning from a different --but possibly somewhat relevant-- field, or are fresh out of school, projects are your friend. Just be certain to briefly describe what the project was for, what you accomplished, and provide metrics. Let’s say that you want to enter the finance field; an example project you can complete is a credit card fraud detector. You’ll use Python to track transaction history and spending habits, and use regression analysis to accurately track the two. You can also include links to your Github profile too, especially if you have a project that’s particularly relevant.

   Talk about collaborations with teams

For those of you who are veterans in the field, focus on your work done with other departments. Data science is all about working with other teams to drive business decisions, and teamwork is a skill that recruiters look for. What collaborative projects have you done that exemplifies this? Are/were you in charge of leading a team that brought in lots of revenue or extra work time? Have you been in charge of a major development project? Detail this information in your experience.

Writing Your Data Scientist Resume: Section By Section

  header, 1. put your name front and center.

Your name should be the most prominent element in your header, typically styled in a larger font than the rest of your contact details. This makes it easy for hiring managers to remember who you are.

Here's an example of how to format your name:

Avoid nicknames or unprofessional email handles:

• Johnny 'The Data Wizard' Smith
• [email protected]

2. Include essential contact details

Under your name, provide your key contact information:

• Phone number
• Professional email address
• Location (City, State)
• LinkedIn URL

Example of how to format this:

[email protected] | 555-123-4567 | Seattle, WA | linkedin.com/in/johnsmith

Avoid providing unnecessary personal details like your full mailing address or multiple phone numbers, which can clutter your header.

3. Optionally include your top data science credential

If you have an impressive, industry-recognized data science certification or credential, consider featuring it after your name to immediately boost your credibility. For example:

John Smith, CFA [email protected] | 555-123-4567 | Seattle, WA | linkedin.com/in/johnsmith

However, avoid listing multiple credentials or irrelevant certifications that may distract from your core qualifications as a data scientist.

  Summary

A resume summary is an optional section that sits at the top of your resume, just below your name and contact information. While not required, it can be a valuable addition for data scientists, particularly those with extensive experience or looking to transition into the field. A well-crafted summary provides context and highlights your most relevant qualifications, setting the stage for the rest of your resume.

When writing your summary, focus on your key strengths, experience, and accomplishments that align with the data scientist role you're targeting. Avoid using an objective statement, as it tends to focus on your goals rather than what you can bring to the employer. Instead, think of your summary as a snapshot of your professional profile, showcasing why you're the ideal candidate for the position.

To learn how to write an effective resume summary for your Data Scientist resume, or figure out if you need one, please read Data Scientist Resume Summary Examples , or Data Scientist Resume Objective Examples .

1. Highlight your technical expertise

As a data scientist, your technical skills are crucial to your success in the role. Use your summary to showcase your proficiency in key areas such as:

• Programming languages (e.g., Python, R, SQL)
• Machine learning algorithms and frameworks
• Data visualization tools (e.g., Tableau, PowerBI)
• Big data technologies (e.g., Hadoop, Spark)

For example:

Data Scientist with 5+ years of experience leveraging Python, R, and SQL to build and deploy machine learning models. Proficient in data visualization using Tableau and PowerBI, with expertise in big data technologies like Hadoop and Spark.

2. Quantify your impact

Hiring managers love to see concrete examples of how you've driven results in your previous roles. Use metrics and data to quantify your impact, demonstrating the value you've brought to your past employers. For example:

• Experienced data scientist with a passion for solving complex problems
• Collaborated with cross-functional teams to develop and implement data-driven solutions

While these statements provide some insight into your experience, they don't give the hiring manager a clear sense of your impact. Instead, try something like:

• Developed machine learning models that increased customer retention by 15% and reduced churn by 20%
• Led a team of 5 data scientists to optimize supply chain processes, resulting in $2M in annual cost savings

3. Showcase your industry knowledge

Demonstrating your understanding of the industry you're targeting can help you stand out from other applicants. Use your summary to highlight your experience working with industry-specific datasets, tools, or challenges. For example:

Data Scientist with 7+ years of experience in the financial services industry. Expertise in developing predictive models for fraud detection, risk assessment, and customer segmentation. Proficient in using industry-specific tools like Bloomberg Terminal and FactSet.

By showcasing your industry knowledge, you demonstrate to the hiring manager that you understand the unique challenges and opportunities within their sector, making you a more compelling candidate.

Action Verbs For Data Scientist Resumes

The field is all about quantifying aand using data. In your resume, you need to explain what you did with the data you have. In the samples, you’ll see examples of action verbs like “implemented”, “developed”, “coached”, and more. Action verbs like these show that you know how to apply the knowledge you have to your work.

For a full list of effective resume action verbs, visit Resume Action Verbs .

Action Verbs for Data Scientist Resumes

How to write a data scientist resume.

Here are step-by-step instructions on how to write an effective resume for a data scientist role. This guide can be used by both entry-level and experienced data scientists as well as data scientist managers.

Basic steps for writing a Data Scientist resume

1.1: place important information in your header.

Place your name at the top of the resume followed by your professional email address, city/country, and phone number. You could also include the job title of your desired role—e.g., Data Analyst—to tailor your resume to the job. It is a good idea to include links to your professional website and online profiles such as LinkedIn and GitHub.

1.2: Select sections that highlight your most relevant experience

A Data Scientist resume needs sections for experience and education. Unless you are a recent graduate, you should list your experience section first. If you have carried out projects that highlight your data analysis skills, you can include a projects section that briefly describes the projects alongside metrics that show what you accomplished.

Use bullet points to showcase your experience as a Data Scientist

2.1: use the [action verb] + [task] + [metric] format for your bulleted points.

A bulleted list of your achievements in the work experience section will make your resume easy for data science hiring managers to skim. Each bullet point should highlight a specific task or achievement from your previous role. Take a look at the bullet point example below: "Modelled user-engagement framework that reduced churn rate using predictive modeling and clustering that reduced churn rate by 40%." Notice how the bullet point uses an action verb that is relevant to data analysis, "Modelled". We describe a task that was completed and use numbers and metrics to quantify the impact of our achievement.

2.2: Highlight collaborative work and initiative

For mid to senior Data Scientist roles, you will need to demonstrate you can take initiative and work with other departments. Talk about collaborating with other teams to drive business decisions. To land a Data Science Manager role, highlight how you led a team to great results in a data science project.

Get past resume screeners by including the right technical skills

3.1: use word or google docs resume template for your draft, then save it as pdf.

Start your resume with a simple template in Word or Google Docs format. This ensures your resume can be scanned easily by Applicant Tracking Systems, which are software used to screen resumes online. Convert your resume to PDF to ensure the formatting and layout appears correctly to a data science recruiter.

3.2: Use an online resume checker to make sure resume scanners can read your resume

If the ATS cannot read your resume, it will automatically discard your application before a Data Science recruiter gets to see it. Upload your resume for free to a resume scanner to ensure it can be read correctly and that the bullet points and sections are correctly constructed.

3.3: Include a technical skills section

Populate the skills section with hard skills and keywords that the resume filtering software will be looking for. Common skills for Data Scientists include Machine Learning, Python, SQL, R, Data Mining, Statistical Modeling, and Hadoop.

Finalizing your Data Scientist resume

4.1: include resume summary if you are changing careers or are a senior level hire.

While resume objectives are outdated and should never be used, a resume summary is an optional section at the top of your resume that can help direct a recruiter's attention to specific skills and achievements not listed in the rest of the resume. The summary can also include transferable skills for people shifting to Data Science from other careers.

4.2: Reread the job description as you edit your resume

When you finish writing your resume, reread the job description. This will give you a sense of how well your resume matches relevant keywords in the data scientist role. Check whether you have included examples of your impact, such as the amount of savings your company experienced because of the machine learning model that you implemented.

Skills For Data Scientist Resumes

Data science is a number-intensive, data-heavy field. It’s one thing to know how to read the data. You also need to convert that data in a way that makes a company’s overall processes smoother. Your list of skills should aid in showing that. Because you’d be using languages like Python or SQL, it’s important to state it beyond the skills section. Where possible, mention how you used these tools in your experience, whether that’s to process large data sets, discover insights or drive business decisions. If recruiters can see that you know how to use critical tools for the job on your resume, it’ll stand out more. Plus, your resume will get past resume screening tools/ATS since employers often filter resumes out by searching for skills they expect to see. Closely read the job description to find skills to include in your resume.

• Data Science
• Machine Learning
• Artificial Intelligence (AI)
• Deep Learning

Data Mining

• Python (Programming Language)
• Natural Language Processing (NLP)
• Apache Spark
• R (Programming Language)
• Predictive Analytics
• Predictive Modeling
• Software Development
• Statistical Modeling

Skills Word Cloud For Data Scientist Resumes

This word cloud highlights the important keywords that appear on Data Scientist job descriptions and resumes. The bigger the word, the more frequently it appears on job postings, and the more 'important' it is.

How to use these skills?

Resume bullet points from data scientist resumes.

You should use bullet points to describe your achievements in your Data Scientist resume. Here are sample bullet points to help you get started:

Conducted private equity due diligence in $400M portfolio. Performed strategic and analytical valuation of assets based on interviews with experts and created extensive models of the industries; persuaded client to move forward with acquisition

Analyzed data from 25000 monthly active users and used outputs to guide marketing and product strategies; increased average app engagement time by 2x, decrease drop off rate by 30%, and increased shares on social media by 3x over 6 months

Generated insights on customer churn and renewal rates from data tables with 100M rows in SQL

Liaised with marketing to drive email and social media advertising efforts, using predictive modeling and clustering, resulting in a 35% increase in revenue

Reduced signup drop-offs from 65% to 15%, increased user-engagement by 40%, and boosted content generation by 15%, through a combination of user interviews and A/B-testing-driven product flow optimization

For more sample bullet points and details on how to write effective bullet points, see our articles on resume bullet points , how to quantify your resume and resume accomplishments .

Frequently Asked Questions on Data Scientist Resumes

How can i improve my data scientist resume.

• Include a projects section that briefly describes the projects alongside metrics that show what you accomplished. Here, list projects that demonstrate the use of statistical methods, data visualization techniques and predictive models.
• Include the job title for the desired role—Data Scientist—on the resume header below your name. This makes your resume easier for screening software to categorize.
• Include links to your professional website and online profiles such as LinkedIn and GitHub.
• Include a summary section if you are a senior-level hire or are changing careers to direct the recruiter’s attention to transferable skills and exceptional achievements.

How does a data scientist’s resume differ from that of other data analytics roles?

What skills should you put on a data scientist resume, what are strong examples of bullet points i can include in my data scientist work experience.

Modelled a user-engagement framework that reduced churn rate using predictive modelling and clustering that reduced churn rate by 40%. Designed and implemented securities forecasting models, improving stock market forecast accuracy by 15%.

Other Data & Analytics Resumes

Director of Analytics

Solutions Architect

• Data Analyst Resume Guide
• Data Engineer Resume Guide
• Business Analyst Resume Guide

Data Scientist Resume Guide

• Data Mining Resume Guide
• Data Entry Resume Guide
• Business Intelligence Resume Guide
• SQL Developer Resume Guide
• Actuarial Science Resume Guide
• Data Modeling Resume Guide
• Supply Chain Planner Resume Guide
• Program Analyst Resume Guide
• Market Researcher Resume Guide
• Big Data Resume Guide
• Intelligence Analyst Resume Guide
• Director of Analytics Resume Guide
• Reporting Analyst Resume Guide
• Data Governance Resume Guide
• Data Specialist Resume Guide
• Machine Learning Resume Guide
• GIS Resume Guide
• Data Scientist Resume Example
• Senior Data Scientist Resume Example
• Entry Level Data Scientist Resume Example
• Data Science Manager Resume Example
• Data Science Vice President Resume Example
• Junior Data Scientist Resume Example
• Career Change into Data Science Resume Example
• Tips for Data Scientist Resumes
• Skills and Keywords to Add
• Sample Bullet Points from Top Resumes
• All Resume Examples
• Data Scientist CV Examples
• Data Scientist Cover Letter
• Data Scientist Interview Guide
• Explore Alternative and Similar Careers

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Entry Level Data Scientist Resume Examples & Writing Guide

The Entry-Level Data Scientist’s Resume Guide for 2024

Here are our most important resume guidelines we recommend all job seekers follow for a significantly better shot at getting invited for an interview.

Here’s what we’re going to cover in this guide:

• How to properly format your resume for success in 2024
• How to talk about your work experience and personal projects
• How to create a competitive skills section
• How to customize your resume for each job you apply to

Formatting a Entry-Level Data Scientist Resume

Reverse chronological structure: Recent experience first

Reverse chronological resume is the most popular which almost all job seekers should be using. This format means listing out the most recent and relevant things first. It starts with the most recent timeline and you work your way backward, e.g. the latest job position comes first in your work experience section, not the oldest. Structuring your resume this way makes your career timeline more established. 

Entry-Level Data Scientist Resume Length

Stick to 1 page.

Most IT Specialists have found that their best shot at landing an offer is by having a one or two-page resume. This includes a collection of sections that cover relevant past experiences. Your resume should be no longer than three pages. The risk of going beyond two pages is including irrelevant content that could distract from your qualifications.

Avoid Common Entry-Level Data Scientist Resume Format Mistakes

1) Resumes must include as much content as possible.

2) Uniform and labeled  section headers

When it comes to creating an ATS-optimized resume, it’s good to know how it will be read by the software. Like most human readers, the ATS will read from left to right and top to bottom. 

3) Minimal white-space

8 ATS IT Resume Examples (Including Writing Tips)

Key Sections for a Entry-Level Data Scientist Resume 

A tailored resume summary .

In a competitive field like Entry-Level Data Scientist, hiring managers sometimes review hundreds of applications for a single position. Writing a resume that stands out is difficult, so solve that problem by including a compelling introduction for your resume. Begin your resume with a resume objective, which is a 2–3 sentence paragraph about your job-relevant skills and career goals.

    Entry-level Data Scientist with 2 years of experience in Python and R for large scale data collection, analysis and reporting. Proven understanding of predictive analytics with existing tools. Applying for a position at Company A to apply knowledge of data analytics to improve business decision making.

     Entry-level Data scientist with 2 years of extensive experience in Java, NoSQL, and Apache Spark. Proficient in web scraping and data cleansing. Proven ability to build and deploy scalable data pipelines. Candidates for Data Scientist role at Company A should possess the following qualities: curiosity, passion for building things, and ability to lead cross-functional teams.

     Entry-level Data Scientist with 3 years of experience in Data Science and Data Analysis. Proven ability to implement and test algorithms using SQL and Python. Outstanding writing and presentation skills. Seeking to leverage data lineage to drive product innovation and growth at Company A.

You can read more about how to create a resume summary with artificial intelligence here.

Your Relevant Professional Experience 

Employers will want to know what you’ve done in your previous job position to get insight into your skills and expertise. Knowing what you’re familiar with will help them make a decision and to see if you’re more of a good fit compared to the other applicants.

However, you should also be including any positive results you’ve accomplished.

Information Technology(IT) Certifications

Emphasize your credentials. Include mention of all Important Skills for Information Technology (IT) Jobs , Certified Analytics  Professional(CAP), Cloudera Data Platform Generalist Certification, Data Science Council of America(DASCA) Senior Data Scientist, IBM Data Science Professional Certificate, Microsoft Certified : Azure AI Fundamentals, Open Certified Data Scientist(Open CDS) or other IT/Programming methods or processes.

Skills for a Entry-Level Data Scientist Resume

Either at the beginning (after the resume summary) or at the end of your resume, insert a tech table that lists the software, hardware, and technical processes with which you are conversant. Be sure to include those that are specifically mentioned in the job ad; these are important “keywords” that will help rank your resume highly when it is scanned by an automated applicant tracking system during its first round of review.

Hard skills like software, tools, and the technical knowledge to interact on websites, developing servers, and databases for website functionality, and coding for different platforms. But don’t forget to include soft skills like teamwork and communication skills, which are necessary to coordinate with a developer, Consultant, Business owner and ui/ux designer. 

Here are more key hard and soft skills that look great on a Entry-Level Data Scientist resume:

Hard skills to consider

• Engineering programs: Python, C/C++, Java, Perl, SQL, R, SAS, Hive, Spark, Big Data, Statistics, Mathematics, Deep Learning, Machine Learning, Data Visualization, Processing Large Data sets, Data Wrangling
• Industry credentials: Certified Analytics  Professional(CAP), Cloudera Data Platform Generalist Certification, Data Science Council of America(DASCA) Senior Data Scientist, IBM Data Science Professional Certificate, Microsoft Certified : Azure AI Fundamentals, Open Certified Data Scientist(Open CDS) 

Soft Skills to consider

• Communication
• Product Understanding
• Adaptability
• Critical Thinking
• Logical Reasoning

IT/Programming Resume Content

Using metrics to quantify experiences.

Don’t simply fill your resume with lists of your Entry-Level Data Scientist job responsibilities. To impress the hiring manager, put your experience in context with specific examples and hard numbers that prove you’re an accomplished IT Specialist.

For instance, list how you Implement data structure measures. How you design, develop and maintain computer software also build systems that collect, manage, and convert raw data into usable information to interpret. How you design user interactions on websites, develop servers, and databases for website functionality, and coding for mobile platforms.

Optimizing Resume Content with IT/Programming Keywords

How to tailor your resume to a job . Many hiring managers use applicant tracking systems (ATS) to filter applications based on resume keywords they write in their job ads. The more Entry-Level Data Scientist –related keywords you can use, the higher the chances your resume gets past the ATS and onto the hiring manager’s shortlist of applications.

The ATS keyword research process doesn’t have to be difficult. In fact, it’s a straightforward process if you’re doing it with Rezi. Our AI Keyword Targeting feature allows you to upload a job description and instantly see which keywords should be included in your resume's content.

Instantly Generate Entry-Level Data Scientist Resume Content

Job seekers can now use AI to create the perfect application with minimal effort. How long does it take to make a resume writes tailored content instantly and prevents writer’s block completely. It’s that simple yet highly effective in helping you get more job offers and interviews faster. 

All you need to do is enter a few details such as:

• Experience level

Learn more about Rezi AI Resume Writer here

Include A Entry-Level Data Scientist Cover Letter

Many job seekers today overlook the importance of a cover letter . The significant impact it can leave on your recruiters will play its part in the decisive moment for whether you’re going to get hired or not.

To put it simply, cover letters can be your gateway to improving your overall job application and a higher chance of getting the job.

Unless your resume is absolutely perfect with no flaws, why settle for less? A cover letter can greatly increase your odds of getting hired for the company you want to work for, even if it’s a competitive job posting.

Learn more about Rezi AI Cover Letter Builder here

Professional resume formats for your

Entry level data scientist resume.

Standard Resume Format

Compact Resume Format

Bold Resume Format

Alternative Resume Format

Data Scientist Resume Example

This guide provides you with Data Scientist resume examples to use to create your own resume with our easy-to-use resume builder. Below you'll find our how-to section that will guide you through each section of a Data Scientist resume and you'll be closer than ever to landing your dream job.

Want to write a great Data Scientist resume?

You should know this. Most data science resumes that hiring managers receive scream:

• “Wrote a digit recognition algorithm with 95% accuracy”
• “Used Tensorflow to do this really simple detection”
• “Used this off the shelf software for ‘X’”

Reality is, most entry level data science resumes rarely go beyond the common pattern listed above. The experienced data science resumes on the other hand fail to communicate the complexity, scale or innovation performed.

Fixing just that would make your data science resume stand out from 90% of the other applications that a hiring manager would receive.

In this guide, we are going to take you a step ahead though. Whether you are looking to land a FAANG/MAANG data science role or work for an innovative startup - we are going to show you how to create a Data Scientist resume that will win 99% of the time!

FAANG Data Scientist Resume Example

Senior Data Scientist Resume Example

Let’s start with an overview of what it takes to create a great Data Scientist resume.

How to write a Data Scientist Resume?

To write a Data Scientist resume:

• Highlight either your business impact or data science innovation.
• Provide context to what type of ML work you performed
• Make sure to add the programming languages you use
• If applicable, show your ability to architect ML systems
• Highlight your publications

If you avoided those, you would struggle to justify how your work made an impact. For example, it isn’t uncommon for us to come across statements like these in data science resumes: “Leverage my skills in data cleaning, data analysis and predictive modeling to achieve business goals” - statements like these are bad for your resume.

However, if you are seeking an entry level data science position - consider the following while writing your entry level data science resume:

• Highlight your thesis and projects - they make a big difference when there’s no work experience.
• While listing your projects, display your thoughtfulness in approaching the problem and solving it.
• Adding programming languages adds weight to your data science resume. However, do not list yourself as an “expert” if you are a recent graduate.
• Add a link to your portfolio or Github.

Do you know about FAANG data science roles - a Github profile is the most commonly sought after resource to see how proactive you are, what you’ve built on your own and your code quality.

The Best Data Science Resume Format

The quality of a good data science resume format would be:

• A format that allows you to list your skills and experience in one (or max two pages).
• Consistent throughout leveraging not more than two fonts and shouldn’t have too many colors on it.
• Uses bullet lists instead of large paragraphs to highlight a Data Scientist’s skills and experience.

Keeping those three qualities of a good Data Scientist resume’s format, the best format for you would be:

• Reverse chronological resume format - if you are an experienced Data Scientist.
• Hybrid resume format - if you are an entry level Data Scientist who lacks the experience, but has skills and data science projects to show.

Experienced Data Scientist’s Resume vs Entry Level Data Scientist’s Resume?

What separates an experienced Data Scientist’s resume from an entry level resume is: #1 Business impact: An entry level Data Science resume can often only display a thoughtful approach to solving a problem, but a job winning Data Scientist resume should be able to show the impact of work performed.

E.g. an entry level Data Scientist resume would have “Leverage data cleaning, database management and deep learning for text classification”

Vs an experienced Data Scientist’s resume would say “Created real time text classification capabilities through hybrid deep learning models (attention mechanism position and focal loss) for City of Chicago to handle traffic violation in low light conditions. Convolution attention mechanism used was Bi-LSTM with CABO model.”

#2 Technically descriptive: As most entry level Data Scientist resumes don’t involve innovating and leveraging sophisticated technologies. It isn’t too difficult to find phrases like “Wrote a machine learning model to recognize Chinese characters”

Vs an experienced Data Scientist’s resume should say “Led digitization of 3TB of Chinese character data by using RAN of aggregation module, mapping encoder and a character analysis decoder. Outperformed existing DenseRAN by 33.6%, with 57.9% higher computing efficiency.”

As you can see, a good data science resume would change radically with the experience of a Data Scientist. But, it isn’t uncommon to see experienced Data Scientists write their resumes as if they are an entry level professional.

When you write meaningfully, a hiring manager not only is able to see the impact you made, but is also able to see if you have worked on similar business or technology projects in the past as theirs.

Data Scientist Resume: Summary or Objective?

Here’s a rule of thumb for you - write a data science resume objective only when you are an entry level professional or when you are transitioning from another role (e.g SWE) to data science. If you are already working as a Data Scientist, write a resume summary instead.

With that in mind, let’s take a look at how to write an excellent Data Scientist resume summary.

How to Write a Data Scientist Resume Summary (with Examples)

To write a great Data Scientist resume summary, include the following information:

• State your years of data science experience (e.g. 10+ years of experience in…”).
• List your top technical specialization (e.g. LSTM, GAN, etc).
• List your top business skills (e.g. customer segmentation, image processing, pricing analysis, market basket analysis, etc).
• Finally, add relevant certifications and awards that you have received.

Let’s check two examples of good and bad Data Scientist resume summary samples that will illustrate better.

Entry Level Data Science Resume Summary - Bad

I am a Data Scientist with experience of analytics and applied data science experience with a focus on strategic initiatives targeting business scalability, process improvement, and efficiency.

Entry Level Data Science Resume Summary - Professional

Data Scientist with 9 months of analytics and applied data science experience to support $100M maintenance operations using survival models and PowerBI dashboards. Business expertise: performance drift, revenue leakage and regression analysis for cost estimation.

In the two Data Scientist resume examples above, we see that both have noticeable entry level experience. But when you read the second Data Scientist’s resume summary, one can clearly see why the second data science resume would win.

If you are an entry level Data Scientist too, here’s a template that you can copy to write your resume summary: “Data Scientist with {x} {months/years} of analytics and applied data science experience to support {operations} using {data science technique}. Business expertise: {expertise 1}, {expertise 2} and {expertise 3}.”

Experienced Data Science Resume Summary - Bad

Experienced Data Scientist experienced in designing, building and deploying fast, accurate, scalable and secure machine learning applications in the cloud.

We list this as a bad data science resume summary mainly because it won’t help you stand out. Let alone beat 99% of the other data science resumes. Every word added to your Data Scientist resume allows you to leave an impact - in this case you won’t make any.

Experienced Data Science Resume Summary - Professional

Data Scientist with 10+ years of experience in building high performing NLP products. Expert at neural architecture optimization of large feature spaces for performance gains. Author of Lin-ML - used by more than 100,000+ machine learning developers.

How to Write a Data Scientist Resume Objective (with Examples)

The most important factors to consider when writing your Data Scientist resume objective are:

• Add your top skills, area of expertise or specialization in it.
• Mention what you are passionate about.
• List your top recognizable achievements.

Entry Level Data Science Resume Objective - Bad

An enthusiastic entry-level data scientist, a NCSU graduate. I have hands-on work experience in machine learning models and a portfolio of Data Science projects.

Entry Level Data Science Resume Objective - Professional

An enthusiastic entry-level data scientist with hands-on work experience in creating RNN and Modular NNs to text and speech problems. Kaggle Master, Top 5% on Stackoverflow for Python and winner of Google Universal Image Embedding challenge(GAN).

When you compare those two Data Science resume examples above it isn’t too hard to see the following:

• Good Data Scientist resumes will be very specific about their past projects and top technologies.
• Poor Data Scientist resumes will be generic or verbose without any specific skills.

Common mistakes to avoid while writing a resume summary or objective include:

• Writing more than 3 lines in a resume summary or objective. If it is a wall of text, it’s going to negatively impact your application.
• Listing yourself as an expert - it is better to let your skills and accomplishments do the job instead.
• Being too vague about your interest and technology used in projects/work experience.

The idea here is to leave a good first impression, a hook that will allow the hiring manager to continue to read further with interest.

Need more examples? Here are 6 Data Scientist resume objective examples .

How to Describe your Data Scientist Experience on Resume?

Describing your data science experience on your resume should not be taken lightly. It is always one of the top few items on a hiring manager’s checklist. Despite that importance, it isn’t uncommon to see very poorly written work history on a Data Scientist’s resume.

To write a winning Data Scientist resume, you should describe your experience by following the STAR method. Using the STAR method it is very easy to highlight a problem you solved, how thoughtful you were in solving the data science problem and what results you achieved.

Let’s checkout a couple of examples to see how

Bad Data Scientist Resume Experience Sample

Data Scienstist

• Worked within the Data Science team in the SF office.
• Taking responsibility for coordinating data partnerships, and improving existing modeling processes.
• Spearheading data for new lines of business.
• Support internal data modeling needs for stakeholders and cross functional teams.
• Utilizing a plethora of technologies in my day-to-day work.

Looking at this Data Scientist’s resume, any hiring manager would wonder:

• If they have the right experience to solve the data science challenges they are looking to solve?
• They failed to communicate the impact of their work - would they be able to communicate their insights in a way that everyone can understand?
• What functions did they serve in this role?

Hiring managers spend as little as 7 seconds scanning a resume. They scan your summary/objective, job titles, work experience and your skills. If they don’t find what they are looking for, they discard your application - all in 7 seconds!

That’s why we suggest you write your work history section in a way that reduces their efforts to find the information they are looking for and leave an impact at the same time.

Let’s now look at a few examples of work history sections of good data science resumes.

Data Scientist Resume Work Experience

Data Scientist

• Optimized existing geospatial query to improve performance by 20%.
• Cleaned car image data with 10,000+ different types of cars to create a new vehicle identification API supporting over 80,000+ car dealerships.
• Worked with compliance teams to implement an AI algorithm (entity resolution algorithm) to protect against cyber threats.
• Data Science lead for DPro (dealer product) initiatives and managed ~20+ data science initiatives.
• Tech stack used: Pandas, PySpark, MCMC, GCP, Databricks, and SQL

Machine Learning Data Science Resume Work Experience

ML Data Scientist

• Created multiple deep neural network architectures to improve robotic instrument segmentation.
• Saved $15.3M in annual spend by deep learning focused histology image analysis with 93.8% accuracy.
• Implemented U-net architecture replacing existing ImageNet neural network with 10.9% higher performance. Consumed by $200M LOB products as of 2022.
• Restructured internal database of >3TB production records to improve performance.

FAANG/MAANG Data Science Resume Work Experience

Meta Data Scientist

• Identified top metrics, collected data, modeled data using SEM, and provided recommendations for the operational performance of 20+ Meta data centers located throughout the world.
• Drive Advertiser value through LSTM implementation and improve the existing understanding of Facebook’s system understanding.
• Risk control - 8.5% higher click-farm identification which led to $10M in wasted ad spend from advertisers.
• Key partner for the product team to collaborate on new insights for the Advertiser product portfolio.

How to Write a Data Science Resume With No Experience?

When you have no data science specific experience, consider writing a section that focuses on your portfolio of data science projects instead. The type of projects that you can include are:

• Recognizable competitions like Kaggle
• Projects listed on your Github profile
• Any significant academic projects performed

Platforms like Kaggle are often used by companies that are hiring entry level/experienced data science talent. And, your Github projects will enable an employer to see what you are capable of, along with your code quality.

Companies like Uber, Microsoft, etc actively collaborate with universities in the form of academic partnerships. That’s why academic data science projects bring in substantial weight to your data science resume for a hiring manager.

How to List your Data Science Projects on Resume?

To list your data science projects on your resume, create a separate section for your projects. For each project add the following information:

• Title of the project
• Short description of the project involving the problem you solved, the solution you used and technology involved.

Data Scientist Resume Example - Projects

Instacart Market Basket Analysis Model building - used XGBoost with two gradient boosted tree models (predicting reorders, predicting zero orders). Characteristic of each of these models include:

• Reorder model - XGBoost with 6 gradient boosted tree models (GBDT, random seed)
• Zero order model - XGBoost with 17 boosted tree models (with a step shrinkage)

Project insights involved:

• Identified patterns where a user won’t repurchase an item.
• Days since reorder plays an important role.
• Items reordered more frequently vs those that aren’t.
• When a user is unlikely to make a reorder.

How to List Your Education on your Data Science Resume

To list your education on your Data Scientist Resume create a new section for education and list your education credentials in it. Your education section should be concise if you are not an entry level Data Scientist.

Example Education Section in an Experienced Data Scientist Resume

Masters in Data Science, 3.9 GPA Texas A&M University

BS, Data Science, 4.0 GPA Texas A&M University

Example Education Section in an Entry Level Data Scientist Resume

• Coursework taken: Big Data 101, GeoSpatial Computing 309 and Machine Learning.
• Thesis: Leveraging GeoSpatial computing with LIDAR data to predict flooding for urban environments.
• Elected as President of Texas A&M Data Science club of 500+ members.

Top 20 Data Science Resume Skills for 2022

• Machine Learning
• Deep Learning
• Data Visualization
• Neural Networks
• Distributed Computing

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6 Data Scientist Resume Examples to Land You a Role in 2023

Data Scientists have an analytical eye and love to break down complex theories and hypothesis into tangible solutions. As a Data Scientist, your resume should track data in an insightful way that delivers an impact just like your solutions do. In this guide, we'll look at 6 Data Scientist resume examples to help position yourself for success in 2023.

Resume Examples

Resume guidance.

• High Level Resume Tips
• Must-Have Information
• Why Resume Headlines & Titles are Important
• Writing an Exceptional Resume Summary
• How to Impress with Your Work Experience
• Top Skills & Keywords
• Go Above & Beyond with a Cover Letter
• Resume FAQs
• Related Resumes

Common Responsibilities Listed on Data Scientist Resumes:

• Develop data mining algorithms and techniques to discover hidden insights from vast amounts of structured and unstructured data.
• Build and deploy machine learning models for predictive analytics.
• Extract, wrangle, and clean data from various sources.
• Research new technologies and solutions to enable data science projects.
• Create interactive data visualizations and summaries to present complex information.
• Analyze and interpret data using descriptive, predictive and prescriptive analytics.
• Work in partnership with stakeholders and other teams to deliver data science solutions.
• Evaluate effectiveness of models and suggest solutions for improvement.
• Develop and implement automated methods and scripts to collect, analyze and report on data.
• Test and deploy models into production environment.
• Lead initiatives to improve identification and correct sources of data quality issues.
• Guide stakeholders on best practices for extracting, combining and validating data.

You can use the examples above as a starting point to help you brainstorm tasks, accomplishments for your work experience section.

Data Scientist Resume Example:

• Developed and implemented machine learning models to improve customer retention, resulting in a 15% increase in customer retention.
• Collaborated with cross-functional teams to develop predictive models to improve business outcomes, resulting in a 20% increase in revenue.
• Led a team of 3 data scientists to develop and implement data-driven solutions to improve business outcomes.
• Created and implemented predictive models to improve customer acquisition, resulting in a 10% increase in new customer acquisition
• Developed and implemented natural language processing models to improve customer service interactions, resulting in a 15% reduction in customer complaints
• Conducted data analysis to identify patterns and trends in customer behavior
• Assisted in the development and implementation of machine learning models.
• Conducted data cleaning and preparation tasks.
• Collaborated with data engineers to develop data pipelines to improve data quality and accessibility.
• Machine Learning
• Predictive Modeling
• Data Analysis
• Data Cleaning and Preparation
• Data Pipelining
• Data Visualization
• Natural Language Processing
• Statistical Modeling
• Algorithms and Optimization
• Big Data Platforms
• Cloud Computing
• Team Leadership
• Business Outcomes Improvement
• Database Design
• Data Mining
• Data Science
• Mathematics

Data Science Fresher Resume Example:

• Developed multiple prototypes and datasets for machine learning applications using Python, R and other languages.
• Constructed numerous data visualizations for statistical analysis and discovered meaningful data insights.
• Presented research findings to peers and management, in a clear and efficient manner, thus increasing general organizational understanding of the data.
• Authored documents and reports to explain complex data analysis results to the wider public
• Attended various conferences and seminars to enhance knowledge of data science and machine learning trends
• Database cleaning and organized production of large datasets for pattern and trend recognition
• Constructed predictive models and algorithms to discover new data collection methods
• Created and validated experiments to gain in-depth knowledge of data-driven solutions
• Spearheaded development of technical documents, which required intense data mining techniques
• R Programming
• Statistical Analysis
• Algorithm Development
• Database Management
• Database Optimization
• Technical Writing
• Presentation & Communication Skills
• Project Management
• Research Methodology
• Artificial Intelligence

Data Science Intern Resume Example:

• Utilized data science tools and techniques to quickly familiarize with the company's datasets and data structures.
• Developed actionable insights from datasets by identifying trends, correlations, and repeatable processes.
• Created predictive models and visualizations to accurately forecast future outcomes, aiding senior leaderships' decisions.
• Leveraged AI, programming languages, and database systems to drive fast and accurate results in data science projects
• Generated up-to-date reports communicating organizational findings, conveying context and relevance effectively
• Applied statistical analyses to evaluate current business performance metrics and draw conclusions
• Streamlined data workflow by cleaning, transforming, and importing data into the company's systems
• Constructed data models, in collaboration with other teams, to enhance organizational insight and potential
• Spearheaded initiatives to provide better and more reliable communication of data analytics to stakeholders
• Creative problem solving
• Proficiency in programming languages (e.g. Python, R, SQL)
• Statistical analysis
• Data mining
• Machine learning
• Data cleaning & transformation
• Data visualization
• Big data manipulation
• Project management
• Technical communication & reporting
• Data driven decision-making
• Business analytics

Entry Level Data Scientist Resume Example:

• Improved database models and querying techniques, increasing query efficiency by 20%.
• Applied machine learning models to forecast customer demand, enabling business to better manage inventory levels.
• Enhanced reporting solutions by developing an innovative data visualization platform, resulting in a 10% increase of meaningful analysis efficiency.
• Automated data analysis pipelines, reducing manual processes and errors by 10%
• Developed A/B tests and experiments to measure the effectiveness of data-driven decisions, leading to a 25% improvement in effectiveness
• Spearheaded the implementation a cybersecurity protocol, safeguarding data and maintaining secure operations
• Built customer segmentation models to enhance the organization’s knowledge of customer demographics and preferences
• Processed and prepared large data sets from four different sources, merging the data into one comprehensive database
• Constructed comprehensive data dashboards for the effective and timely visualization of data, increasing work efficiency by 20%
• Database Modeling
• A/B Testing
• Cybersecurity
• Segmentation Modeling
• Data Preparation
• Data Dashboards
• Data Wrangling
• Programming
• Logical Thinking
• Communication
• Problem Solving
• Time Management
• Attention to Detail

Junior Data Scientist Resume Example:

• Achieved a 20% increase in overall efficiency by revamping existing queries and data models built in SQL and R
• Used a combination of Python and Tableau to develop dynamic dashboard visualizations of key data performance trends
• Automated processes to analyze and report on project results, enabling stakeholders to view up-to-date KPIs in real-time
• Implemented new analytical methodologies and machine learning models to optimize data analysis on large datasets
• Enabled secure data access to over 50 stakeholders across corporate departments, increasing collaboration between teams
• Developed an intelligent BI system for predictive analytics, improving the accuracy of data predictions by 45%
• Predictive Analytics
• Data Manipulation
• Dashboard Design
• Data Quality & Governance
• Structured Data
• AI & Automation
• Data Security & Accessibility
• Multivariate Analysis
• Data Warehousing
• Database Design & Architecture
• Big Data Analytics

Senior Data Scientist Resume Example:

• Spearheaded the creation of an advanced predictive model to forecast customer trends, producing an 8% increase in accuracy from previous models and driving a 15% growth in overall revenue.
• Developed features from raw data gathered from multiple sources and utilized BI technologies, big data, and machine learning techniques to improve data modeling results.
• Led a team of 5 junior data scientists in developing an innovative research and development pipeline, resulting in an increase of 10% in the company's product offering accuracy.
• Redesigned existing data models in order to achieve a 10% increase in accuracy and a 5% cost savings
• Collaborated with engineers and software developers to deploy newly created models into production, achieving a 40% decrease in the time to market
• Employed neural networks, decision trees, and deep learning algorithms to generate predictive models that resulted in a 25% increase in target user engagement
• Authored an effective iteration of the company’s customer acquisition strategy that increased inbound leads by 30%
• Leveraged structured and unstructured data to analyze customer behavior, identifying insights that led to a 25% decrease in customer churn
• Produced features from raw data and created visualizations to support executive decisions; resulted in a 20% increase in the team’s success rate
• Statistical modeling
• Natural language processing
• Neural networks
• Deep learning algorithms
• Business intelligence
• Data wrangling
• Feature engineering
• Generative algorithms
• Predictive modeling
• Data analysis
• Pattern recognition
• Probabilistic reasoning
• Model deployment
• Research and development pipeline management
• UI/UX development
• Database optimization
• Data engineering
• Cloud computing

High Level Resume Tips for Data Scientists:

Here are some tips to help Data Scientists get into the right mindset for the resume creation process: Highlight your data-driven mindset: Data scientists are highly analytical thinkers, so you want your resume to showcase your ability to launch data-driven projects and initiatives. Use specific numbers and results to demonstrate the impact of your work. Emphasize quantitative skills: Data Scientists have a strong set of quantitative skills, so make sure to prioritize these when crafting your resume. Highlight your experience with quantitative analytics, statistical modeling, Machine Learning, and data mining. Know your technical skill set: Showcase your technical skillset, such as experience with programming languages, databases and frameworks associated with data science. Also, list any certifications you have or software you’re proficient in. Focus on business objectives: Your data science role is about much more than simply crunching numbers. Use your resume to showcase your ability to identify business objectives and effectively translate them into data-driven projects. Tailor your resume to the job and company: Customize your resume to each job you apply for, emphasizing the skills and experiences that make you the perfect fit for the specific role and company. This can help you stand out from the competition.

Must-Have Information for a Data Scientist Resume:

Here are the essential sections that should exist in a data scientist resume:

• Contact Information
• Resume Headline
• Resume Summary or Objective
• Work Experience & Achievements
• Skills & Competencies

Additionally, if you're eager to make an impression and gain an edge over other data scientist candidates, you may want to consider adding in these sections:

• Certifications/Training

Let's start with resume headlines.

Why Resume Headlines & Titles are Important for Data Scientists:

Data scientist resume headline examples:, strong headlines.

Experienced Data Scientist with 4+ Years of Machine Learning and Knowledge Science expertise

Accomplished Data Scientist demonstrated success in Statistical Modelling and Artificial Intelligence

The good headlines provide concrete and relevant details about the Data Scientist’s experience, qualifications, and accomplishments.

They help clearly distinguish the applicant from other Data Scientists who may be applying for the same job.

Weak Headlines

Highly Skilled Data Scientist

Data Scientist looking for a new challenge

The bad headlines are too broad and don’t give any concrete information about the candidate. They also don’t demonstrate any professional or academic achievements.

Writing an Exceptional Data Scientist Resume Summary:

A resume summary is a critical component of a Data Scientist's resume, providing a succinct overview of their skills, experience, and accomplishments in the field. As a Data Scientist, your summary should emphasize your expertise in data analysis, modeling, and machine learning, as well as your ability to extract insights from complex data sets and communicate findings to stakeholders.

Here are a few tips for writing an effective summary for a Data Scientist:

• Tailor the summary to the specific job you are applying for by highlighting the most relevant skills and experiences.
• Include quantifiable achievements, such as improving predictive accuracy, increasing revenue through data-driven decision making, or implementing new data-driven processes.
• Use relevant technical terms and keywords to show your proficiency in the field and to make your resume stand out to both humans and applicant tracking systems (ATS).
• Keep the summary concise and to-the-point, around 4 sentences or less.
• Avoid using technical jargon that might be difficult for non-technical readers to understand.

Data Scientist Resume Summary Examples:

Strong summaries.

• Experienced Data Scientist with 6+ years of experience in developing and deploying predictive models for a variety of industries. Skilled in data analysis, machine learning, and statistical modeling to drive insights from complex datasets.
• Proactive and detail-oriented Data Scientist with 6+ years of experience in leveraging data to develop analytical insights for business decision making. Adept at programming in Python and R, and utilizing various data visualization tools to communicate findings.

Why these are strong:

• Both summaries are concise, feature the required experience, and provide specific examples of skills and expertise. This provides the reader with a clear understanding of the Data Scientist's abilities and experience.

Weak Summaries

• Experienced Data Scientist with 6+ years of experience. Proficient in data analysis, machine learning, and statistical modeling.
• Data Scientist with 6+ years of experience. Skilled in analytics and data visualization.

Why these are weak:

• These summaries are too vague and lack detail. They do not provide any concrete examples of the Data Scientist's experience or abilities, which would give the reader a better sense of their qualifications.

Resume Objective Examples for Data Scientists:

Strong objectives.

To leverage 2 years of versatile experience, including implementing machine learning algorithms and coding in Python, to contribute to a data science team that supports innovative solutions.

To leverage strong analytical and technical abilities to develop effective data models, visualize data, and uncover insights that drive organizational success.

• What makes the great objectives great is that they concisely emphasize the candidate's experience, technical knowledge, and desire to use their skills to contribute to organizational success.

Weak Objectives

To use my education and experience to help generate profits.

To bring my 3 years of experience in data science to a successful or growing organization.

• These resume objectives are weak because they don't effectively demonstrate the technical knowledge and experience of the candidate. The first objective does not adequately communicate the skills that the candidate has to offer. The second does not indicate how the candidate will drive value for the company.

Use the Resume Summary Generator

Speed up your resume creation process with the ai resume builder . generate tailored resume summaries in seconds., how to impress with your data scientist work experience:, best practices for your work experience section:.

• Share detailed yet succinct descriptions of accomplishments and work experience. Demonstrate how you have used data science to make an impact in the organization, such as in increasing revenue or reducing costs.
• Highlight data-driven methodologies you have employed, such as machine learning, artificial intelligence, big data, and statistical analysis.
• Include a separate section for project highlights and highlight the most notable projects that you have worked on, such as successful predictive analytics projects.
• Demonstrate expertise in troubleshooting and debugging systems, as well as software engineering, if relevant.
• Showcase your collaborative capabilities by highlighting those projects you have initiated and those you have worked on with teams.
• Mention your communication skills by citing situations where you have led data science presentations, organized workshops, and authored reports or white papers.
• Illustrate the extent of your knowledge and experience with programming languages, software packages, and tools used in data science.
• Detail your experience in data warehousing and deployment, as well as data visualization processes.
• Demonstrate your business acumen by emphasizing the successes you have achieved that connected data science solutions with research and development projects, set goals, and improved customer satisfaction.

Example Work Experiences for Data Scientists:

Strong experiences.

Developed and deployed machine learning models that enabled a healthcare company to predict which patients were at high risk of hospital readmission, resulting in a 15% reduction in readmission rates.

Designed and implemented A/B tests that evaluated the impact of different product features on user engagement and revenue, leading to a 20% increase in revenue for a fintech startup.

Conducted exploratory data analysis and developed visualizations that identified key trends and insights in customer data, resulting in data-driven recommendations for improving customer experience.

Developed and implemented a deep learning algorithm that achieved state-of-the-art accuracy on a computer vision task, resulting in a publication in a top-tier conference.

Led a team of data scientists and engineers to develop and deploy a scalable recommendation system for a large e-commerce platform, resulting in a 10% increase in user engagement and revenue.

Conducted statistical analyses and designed experiments to evaluate the effectiveness of marketing campaigns, resulting in data-driven recommendations for improving campaign performance.

• These work experiences are strong because they provide specific and quantifiable examples of the Data Scientist's contributions and impact in previous roles. They demonstrate the individual's technical expertise and ability to solve complex problems, as well as their ability to communicate findings and recommendations to stakeholders. Additionally, they highlight the individual's leadership and collaboration skills, which are important for senior-level positions.

Weak Experiences

Conducted analyses on company data and presented findings to the executive team

Collaborated with stakeholders to identify business needs and develop data-driven solutions

Developed models to analyze customer behavior and recommend strategies for improving customer engagement

Cleaned and pre-processed data for analysis

Developed machine learning models for predicting customer behavior and tested model accuracy

Visualized data and presented insights to stakeholders

• The first weak work experience is too general and lacks specific details about the data analyzed, the techniques used, and the impact of the analyses. It also does not demonstrate the candidate's ability to work with complex data sets or communicate findings effectively. The second weak work experience also lacks specific details about the data analyzed, the techniques used, and the impact of the analyses. It also does not demonstrate the candidate's ability to collaborate with stakeholders or develop effective data-driven solutions.

Top Skills & Keywords for Data Scientist Resumes:

Top hard & soft skills for data scientists, hard skills.

• Computer Programming
• Machine Learning Algorithms

Soft Skills

• Problem-solving
• Critical Thinking
• Interpersonal Skills
• Adaptability
• Presentation Skills
• Written and Verbal Communication
• Organization

Go Above & Beyond with a Data Scientist Cover Letter

Data scientist cover letter example: (based on resume).

Dear Hiring Manager, I am excited to apply for the Data Scientist position at [Company]. With my extensive experience in developing and implementing machine learning models, collaborating with cross-functional teams, and leading a team of data scientists, I am confident that I have the skills and expertise needed to drive successful data-driven solutions for your company. At my previous position, I developed and implemented machine learning models to improve customer retention, resulting in a 15% increase in customer retention. I also collaborated with cross-functional teams to develop predictive models to improve business outcomes, resulting in a 20% increase in revenue. Leading a team of 3 data scientists, I was able to drive successful data-driven solutions to improve business outcomes. In addition to my technical skills, I am a proactive problem solver and excellent communicator. My ability to identify patterns and trends in customer behavior through data analysis, and develop natural language processing models to improve customer service interactions, resulted in a 15% reduction in customer complaints. As a data scientist, I have experience in conducting data cleaning and preparation tasks, and collaborating with data engineers to develop data pipelines to improve data quality and accessibility. My expertise in these areas will allow me to efficiently and effectively contribute to your team. Thank you for for reviewing my resume and considering my application for the Data Scientist position at [Company]. I am excited at the prospect of contributing my skills and expertise to your team and look forward to discussing my application with you further. ‍

Sincerely, [Your Name]

A cover letter is a valuable tool for any job seeker, and this is especially true for data scientists. Data science is a highly competitive field, and a cover letter can help you stand out from other applicants. It can showcase your communication skills, highlight your relevant experience, and demonstrate your enthusiasm for the position.

While a resume provides a summary of your skills and experience, a cover letter allows you to personalize your application and connect with the hiring manager on a deeper level. It's an opportunity to tell your story, explain why you're passionate about data science, and show how you can add value to the organization.

Here are some of the key reasons for pairing your data scientist resume with a cover letter:

• It demonstrates your communication skills: As a data scientist, communication is key. Your cover letter provides an opportunity to showcase your ability to write clearly and concisely, and to convey your ideas effectively.
• It shows your enthusiasm for the position: A well-written cover letter can demonstrate your passion for the role and the organization. This can make a big difference in the hiring manager's decision-making process.
• It highlights your relevant experience: Your cover letter allows you to explain how your skills and experience align with the requirements of the job. This can help the hiring manager understand why you're a good fit for the role.
• It sets you apart from other applicants: A well-crafted cover letter can help you stand out from other applicants who may have similar experience and qualifications.

We understand that writing a cover letter may seem daunting, but it doesn't have to be. Remember that the cover letter is an extension of your resume, so you can use the same format and content as your resume. It's also a chance to address any gaps or questions that the hiring manager may have after reading your resume.

Tips for aligning your cover letter with your resume:

• Use the same header as your resume: This will help the hiring manager identify your application as a complete package.
• Align the content of your cover letter with the requirements of the job: Use the job description as a guide to highlight your relevant skills and experience.
• Use keywords from the job posting: Incorporate relevant keywords from the job posting to help your application get past applicant tracking systems (ATS).
• Keep your cover letter concise and focused: Aim for one page and avoid repeating information from your resume.
• Proofread carefully: Errors in your cover letter can undermine your credibility, so make sure to proofread carefully before submitting your application.

Resume FAQs for Data Scientists:

How long should i make my data scientist resume.

When crafting a resume for a Data Scientist, it's important to keep it concise, concisely highlighting the most important and relevant skills, education, and experience. A general rule of thumb is to keep a resume one page in length, maximum two if absolutely necessary. Ideally, keep each section short and to the point, avoiding lengthy, excessive detail. Remember, Data Scientists should focus on creating a succinct, impactful resume that demonstrates their qualifications and value.

What is the best way to format a Data Scientist resume?

The best way to format a Data Scientist resume is to create sections for Summary, Technical Skills, and Work History/Projects. Within each section, organize bullet points with succinct, descriptive language that highlights relevant achievements. Use a simple, elegant font and structure the document for easy skimming. Include contact information and a professional headshot at the top for a polished look.

Which Data Scientist skills are most important to highlight in a resume?

Data Scientists should include the following hard skills in their resume: 1. Programming: Data Scientists should have strong knowledge in programming languages like Python, R, Java and C++. They should be highly proficient in scripting and they should have experience in a variety of databases like Mysql, MongoDB, Spark, and Hadoop. 2. Data Analysis: Data Scientists should demonstrate expertise in data analysis, data mining, machine learning and statistical modeling. They should have experience in performing exploratory data analysis, interpreting data patterns and building predictive models. 3. Data Visualization: Data Scientists should have strong knowledge in data visualization and be able to create visually appealing and interactive data visualizations using tools like Tableau, PowerBI and D3.js. 4. Communication: Data Scientists should be able to effectively communicate complex ideas to both technical and non-technical audiences and present data-driven solutions in a clear and concise manner. 5. System Engineering: Data Scientists should possess a basic understanding of system engineering, including the ability to setup and maintain complex data pipelines and ETL processes.

How should you write a resume if you have no experience as a Data Scientist?

If you have no experience as a Data Scientist, focus on articulating the skills, qualities and relevant education that make you an ideal candidate. Highlight transferable skills you've developed in any prior work or academic experience that demonstrates your aptitude for working in the field. Also emphasize any relevant projects you've completed that demonstrate your analytical abilities. You can discuss membership in organizations that are related to data science, or any certificates you have earned in data-related fields. Finally, be sure to include the technical details that reflect your understanding of languages and databases commonly used in data science roles.

Compare Your Data Scientist Resume to a Job Description:

• Identify opportunities to further tailor your resume to the Data Scientist job
• Improve your keyword usage to align your experience and skills with the position
• Uncover and address potential gaps in your resume that may be important to the hiring manager

Related Resumes for Data Scientists:

Data science fresher resume example, data science intern resume example, entry level data scientist resume example, junior data scientist resume example, senior data scientist resume example, data scientist resume example, more resume guidance:.

How To Create An Impressive Data Science Resume For Entry Level Jobs

Crafting a resume that lands interviews for coveted entry level data science roles can be challenging, especially for recent graduates or career switchers new to the field. This comprehensive guide provides expert tips and examples for developing an impressive data science resume tailored to entry level opportunities.

It covers key sections and content to highlight, formatting best practices, important skills and keywords, and mistakes to avoid.

If you’re short on time, here’s a quick answer: An effective entry level data science resume should showcase relevant coursework, projects, and skills in statistical programming, machine learning, and analyzing large datasets .

Quantify achievements, optimize with key tech/data buzzwords, and highlight both hard and soft skills to demonstrate potential.

Crafting an Impactful Data Science Resume Objective or Summary

When applying for entry-level data science jobs, your resume objective or summary is your chance to make a strong first impression. This section should effectively communicate your skills, qualifications, and career goals to potential employers.

Here are some tips to help you craft an impactful data science resume objective or summary:

What to Include in a Resume Objective/Summary

Your resume objective or summary should be concise yet compelling. It should clearly state your career goals and highlight your relevant skills and qualifications. Here are some key elements to include:

• Your career goals: Clearly state your objective or aspiration in the field of data science. For example, you might mention your desire to apply your analytical skills to solve complex business problems.
• Skills and qualifications: Highlight the technical skills and knowledge you possess, such as proficiency in programming languages like Python or R, experience with machine learning algorithms, and familiarity with data visualization tools.
• Educational background: Include your degree, major, and any relevant coursework or projects. If you have completed any data science certifications or attended relevant workshops, mention those as well.
• Relevant experience: If you have any previous work experience or internships in the field of data science, briefly mention your responsibilities and achievements. Even if you don’t have direct experience, highlight any transferable skills or experiences that demonstrate your ability to excel in a data-driven environment.

Customizing for Specific Data Science Roles

When applying for different data science roles, it’s important to tailor your resume objective or summary to match the specific requirements of each position. Research the job description and company to understand what skills and qualifications they are seeking.

Here are a few tips for customizing your objective or summary:

• Keywords: Incorporate relevant keywords from the job description into your objective or summary to show that you have the specific skills they are looking for.
• Highlight relevant experiences: Emphasize any experiences or projects that align with the requirements of the role. For example, if the job focuses on natural language processing, mention any projects you have worked on in that area.
• Show enthusiasm: Demonstrate your passion for the field and the company by expressing your excitement for the opportunity to contribute to their data science team.

Example Objective and Summary Statements

Here are a couple of examples to give you an idea of how to craft your own data science resume objective or summary:

Objective: Highly motivated data science graduate seeking an entry-level position where I can apply my strong analytical skills and knowledge of machine learning algorithms to solve real-world business problems.

Eager to contribute to a dynamic team and leverage data-driven insights to drive innovation and growth.

Summary: Recent data science graduate with a strong foundation in programming, statistical analysis, and data visualization. Proficient in Python and R, with experience in applying machine learning algorithms to analyze complex datasets.

Passionate about leveraging data to drive strategic decision-making and optimize business performance.

Remember, your resume objective or summary is your chance to make a strong impression and stand out from other applicants. Tailor it to the specific role you are applying for, highlight your relevant skills and experiences, and showcase your enthusiasm for the field of data science.

Highlighting Relevant Data Science Skills and Keywords

When creating an impressive data science resume for entry-level jobs, it is crucial to highlight your relevant skills and keywords that will catch the attention of potential employers. By showcasing your expertise in technical skills, soft skills, and utilizing key buzzwords, you can demonstrate your proficiency in the field and increase your chances of landing a job in data science.

Technical Skills to Include

Employers in the data science industry are looking for candidates with a strong foundation in technical skills. Some essential technical skills to include on your resume are:

• Proficiency in programming languages such as Python, R, or SQL
• Knowledge of statistical analysis and modeling techniques
• Experience with data visualization tools like Tableau or Power BI
• Familiarity with machine learning algorithms and frameworks
• Understanding of big data technologies such as Hadoop or Spark

By highlighting these technical skills, you are showing potential employers that you have the necessary knowledge and tools to excel in the field of data science.

Soft Skills Valued in Data Science

In addition to technical skills, employers also value candidates with strong soft skills. These skills can demonstrate your ability to work effectively in a team and communicate complex ideas. Some important soft skills to include on your resume are:

• Analytical thinking and problem-solving
• Strong communication and presentation skills
• Attention to detail and accuracy
• Ability to work independently and in a team
• Adaptability and willingness to learn new technologies

By showcasing your soft skills, you are demonstrating that you not only have the technical expertise but also the interpersonal skills necessary to succeed in the field of data science.

Optimizing with Key Buzzwords

When crafting your data science resume, it is important to optimize it with key buzzwords that are commonly used in the industry. These buzzwords can help your resume stand out and show that you are familiar with current trends and technologies. Some popular buzzwords in data science include:

By incorporating these buzzwords into your resume, you are showing that you are up-to-date with current industry trends and technologies, making you a more attractive candidate to potential employers.

Remember, creating an impressive data science resume is all about showcasing your relevant skills and keywords. By highlighting your technical skills, soft skills, and utilizing key buzzwords, you can make your resume stand out and increase your chances of landing an entry-level job in data science.

Featuring Academic Projects and Courses

Describing relevant coursework.

When creating an impressive data science resume for entry-level jobs, it is important to feature your academic projects and courses. One way to do this is by describing the relevant coursework you have completed.

Highlight the courses that are directly related to data science, such as statistics, machine learning, and data mining. Provide a brief summary of the topics covered in these courses and any hands-on experience you gained.

This will show potential employers that you have a solid foundation in data science.

Detailing Practical Data Science Projects

In addition to showcasing your coursework, it is crucial to detail practical data science projects you have completed during your academic journey. These projects demonstrate your ability to apply the knowledge and skills you have acquired.

Include a brief description of each project, the techniques and tools used, and the results achieved. Be sure to highlight any unique approaches or challenges you encountered. This will give employers a better understanding of your capabilities and problem-solving skills.

Linking to Project Code and Examples

To further enhance the impact of your data science resume, consider linking to the project code and examples you have developed. This allows employers to see your work firsthand and assess your technical proficiency.

Provide URLs or GitHub repositories where your code and project documentation can be accessed. Additionally, include any relevant visualizations or data analysis outputs that you have created. This not only validates your skills but also adds a visual element to your resume, making it more engaging and memorable.

Remember, when featuring your academic projects and courses, make sure to prioritize those that are most relevant to the position you are applying for. Tailor your descriptions to highlight the skills and knowledge that align with the job requirements.

By showcasing your academic achievements in data science, you can greatly increase your chances of landing an entry-level job in this exciting field.

Listing Work Experience and Leadership

When creating an impressive data science resume for entry-level jobs, it is important to effectively list your work experience and highlight any leadership positions you have held. This section of your resume allows employers to see your practical experience and how you have contributed to previous organizations.

Including Internships and Volunteering

One way to showcase your work experience is by including any relevant internships or volunteering experiences you have had. These opportunities provide valuable hands-on experience in the field of data science and demonstrate your dedication and commitment to learning.

Be sure to mention any specific projects or tasks you were involved in during these experiences, as this will give employers a better understanding of your skills and abilities.

Emphasizing Transferable Skills

While you may not have extensive work experience in the data science field as an entry-level candidate, you can still emphasize transferable skills that are relevant to the role. For example, if you have experience in coding or programming languages such as Python or R, be sure to highlight this on your resume.

Additionally, skills such as problem-solving, critical thinking, and attention to detail are highly valued in the data science industry, so be sure to showcase these skills as well.

Showcasing Leadership Positions

If you have held any leadership positions, whether it be in a student organization or a part-time job, it is important to showcase these experiences on your resume. Leadership positions demonstrate your ability to take initiative, manage teams, and make important decisions.

These qualities are highly sought after in the data science field, as professionals often work in collaborative environments and need to effectively communicate and lead others.

According to a study conducted by LinkedIn, 41% of hiring managers consider leadership experience as a crucial factor when evaluating entry-level candidates for data science positions.

By effectively listing your work experience and highlighting any leadership positions you have held, you can create an impressive data science resume that stands out to employers. Remember to tailor your resume to each job application, focusing on the experiences and skills that are most relevant to the specific role you are applying for.

Formatting and Design Best Practices

Using clear, scannable formatting.

When it comes to creating an impressive data science resume, formatting is key. Hiring managers often receive a large number of applications, so it’s important to make your resume easy to read and scan.

Use clear headings and subheadings to organize your information and make it easier for the reader to navigate. Use bullet points to highlight your skills and achievements, and use a consistent font and formatting style throughout the document.

Remember, you want to make it as easy as possible for the hiring manager to quickly grasp your qualifications and the value you can bring to the position.

Selecting the Right Length

When it comes to resume length, it’s important to strike a balance. While you want to include all relevant information, you also don’t want to overwhelm the reader with a lengthy document. For an entry-level data science resume, it’s generally recommended to keep it to one page, unless you have extensive experience or additional relevant certifications.

Keep in mind that hiring managers typically spend just a few seconds scanning each resume, so it’s crucial to prioritize the most important information and keep it concise and impactful.

Crafting Section Headings Strategically

Section headings are an essential part of your resume’s organization and can help highlight your qualifications and achievements. When crafting your section headings, be strategic in your approach. Use clear and concise headings that accurately reflect the content of each section.

For example, instead of using a generic heading like “Work Experience,” consider using specific headings like “Data Science Internship” or “Research Assistant” to showcase your relevant experience. Additionally, consider using bold or a larger font size to make your section headings stand out and catch the reader’s attention.

Remember, your resume is your chance to make a strong first impression and stand out from the competition. By using clear, scannable formatting, selecting the right length, and crafting section headings strategically, you can create an impressive data science resume that grabs the attention of hiring managers and increases your chances of landing that entry-level job.

For more tips and examples of well-formatted resumes, check out websites like The Muse or Indeed .

In summary, an exceptional data science resume for entry level candidates highlights technical expertise, soft skills, hands-on projects, and a passion for data to stand out. A tailored resume objective, measurable achievements, optimizing keywords, and strong presentation will demonstrate value to employers hiring for data science roles.

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Data Scientist Resume - Sample & Guide for 2024

You’re a data scientist. You solve complex problems.

Your newest problem: writing a resume for that elusive data scientist role.

Fortunately, you’ve arrived at the best place. This guide will take you through a range of steps, so you can create a data scientist resume that gets results. 

• An example of a finished data scientist resume that works
• How to write a data scientist resume that’ll fill up your interview diary
• How to make your data scientist resume stand out [with top tips & tricks]

Before we get stuck into the data, here’s a data scientist resume example, created with our very own online resume builder :

This resume performs as well as it looks. Just follow the steps in this guide to create a data scientist resume that gets great results, just like the above example.

Besides our data scientist resume example, we've got even more resume examples for professionals in the computer science field:

• Data Analyst Resume
• Data Entry Resume
• Computer Science Resume
• Artificial Intelligence Engineer Resume
• Engineering Resume
• Software Engineer Resume
• Web Developer Resume
• Java Developer Resume

How to Format a Data Scientist Resume

Before you can reveal why you’re the best person for the job, you need to pick the best format.

Now, this is more important than it sounds.

It will allow your best attributes to ‘jump off the page’ into the recruiters' vision. 

The most common resume format is “ reverse-chronological ”, and it’s for good reason. Essentially, it allows the recruiter to immediately see the value that you provide. We recommend the majority of individuals start with this format.

The following resume formats also get our approval:

• Functional Resume – If you have strong skills, but a weak work history, then this resume format is recommended. It’s ideal for skilled scientists that don’t have a lot of experience or have gaps in their employment history
• Combination Resume – Acting as a combination of both the “Functional” and “Reverse-Chronological” formats, you can use a combination resume if you have a wealth of work experience

Once you’ve chosen your format, you need to organize your resume layout .

Use a Data Scientist Resume Template

As a data scientist, you present data in a structured way.

The same needs to happen to your resume.

However, creating a structured file isn’t an easy task!

You could use Word, but then you will have to risk the layout falling apart with every small alternation. 

Want to skip formatting issues? Use a data scientist resume template .

What to Include in a Data Scientist Resume

The main sections in a data scientist resume are:

• Work Experience
• Contact Information

Want to go a step further? You can also add these optional sections:

• Awards & Certification

Interests & Hobbies

What should you write for each section? 

Read on to learn how.

Want to know more about resume sections? View our guide on What to Put on a Resume .

How to Correctly Display your Contact Information

Now, there is no need to get creative in this section. 

The only requirement is accuracy. 

An incorrect contact section may mean the recruiter can’t contact you – disaster! 

The contact information section on your resume must include:

• Title – In this case, “Data Scientist”
• Phone Number – Check this multiple times for errors
• Email Address – Use a professional email address ([email protected]), not your childhood email ([email protected]).
• (Optional) Location - Applying for a job abroad? Mention your location.
• Ellie Branning, Data Scientist. 101-358-6095. [email protected]
• Ellie Branning, Data Scientist Whizz. 101-358-6095. [email protected]

How to Write a Data Scientist Resume Summary or Objective

It’s safe to say that recruiter’s don’t have time to dig into the data of every resume.

Instead, they scan the resume for the main points.

In fact, studies have shown that recruiters spend just a few seconds on each resume! 

So, what can you do?

You need an introduction that makes your value ‘jump off the page’.

To do this, use a resume summary or objective .

These are snappy paragraphs that go on top of your resume, just under your contact information. 

Now, this section is extremely important. This small paragraph could be the deciding factor between scoring an interview and simply having your resume dismissed.

But what is the difference between the two sections?

A resume summary is a 2-4 sentence summary of your professional experiences and achievements.

Certified data scientist with 12 years of experience for a diverse clientele. Achievements include updating data streaming processes for an 18% reduction in redundancy, as well as improving the accuracy of predicted prices by 18%. Highly-skilled in data visualization, machine learning, leadership.

A resume objective is a 2-4 sentence snapshot of what you want to achieve professionally.

Motivated data scientist with 2+ years of experience as a freelance data scientist. Passionate about building models that fix problems. Relevant skills include machine learning, problem solving, programming, and creative thinking.

So, which one is best, summary or objective?

Generally, we recommend that experienced data scientists go with a resume summary. Those who are new to the field, like graduates and career changers, would be better suited to an objective. 

How to Make Your Data Scientist Work Experience Stand Out

Recruiters need to be confident that you will do a good job for the company.

Listing your work experience is the easiest and best way to do this.

Here’s the best way to structure your work experience section:

• Position name
• Company Name
• Responsibilities & Achievements

Data Scientist

03/2016 - 05/2019

• Improved the accuracy of predicted prices by 18%.
• Coordinated a team of 16 data scientists working on 4 different projects.
• Updated data streaming processes for a 18% reduction in redundancy.

To separate your resume from the other applicants, you should talk about your best achievements, not your daily tasks. Doing so will clearly show how you can benefit the company.

Instead of saying:

“Data streaming.”

“Updated data streaming processes for an 18% reduction in redundancy.”

As you can see, the first statement doesn’t effectively convey your achievements. It shows that you streamed data, but it doesn’t show the results of your work. 

The second statement shows that you managed to reduce the redundancy numbers. Hard numbers that prove your skills – can’t argue with that!

What if You Don’t Have Work Experience?

Maybe you’re trying to break into the data science field?

Or maybe, you have already worked in the industry, but never in this specific role?

Your experience is null .

A recruiter will want data scientists that they can rely on. Whether you have job experience or not, being able to show that you have the skills is the most important factor.

If you already have proof of your data science skills, feel free to link to them in your resume.

With that said, there is still time to create a portfolio.

Here are several ways you can show your talents (and even get paid for it):

• Start freelancing.
• Offer your skills to friends and family.
• Contribute to open source projects on GitHub.
• If the above doesn’t work, become your own client! Show your skills by creating mock projects.

Are you recent data scientist graduate? Make sure to check out our student resume guide !

Use Action Words to Make Your Data Scientist Resume POP!

…are all common words that the recruiter sees time and time again.

However, you want to separate your resume from the competition, which means using power words to make your achievements stand out:

• Conceptualized
• Spearheaded

How to Correctly List your Education

Every great resume needs an education section.

But don’t worry, there is nothing too complicated here.

Simply enter your education history in the follow format:

• Degree Type & Major
• University Name
• Years Studied
• GPA, Honours, Courses, and anything else you might want to add

BSc in Statistics

University of Bath

2012 - 2016

• Relevant Courses: Probability and Statistics, Generalised Linear Models, Applied Statistics

Now, you may have some questions on this section. If so, here are the answers to some of the most frequent questions that we get:

• What if I haven’t finished education yet?

Regardless of whether you’re a data science graduate or still studying, you should mention all years studied to date

• Should I include my high school education?

The general rule is to only include your highest form of education. So, include your high school education if you don’t have a relevant degree for data science

• What do I put first, my education or experience?

Experiences are the priority, so those go first. If you’re a recent graduate, you will likely need to start with education.

Need to know more? Check out our guide on how to list education on a resume .

Top 15 Skills for a Data Scientist Resume

When it comes to the skills section, the hiring manager has seen it all before.

In fact, they need a data scientist to help with the entire pile of data scientist resumes!

You see, everyone lists all of their skills, even those that related to the job.

Your skill section should highlight your top skills in a way that is specific to the role.

Here are some of the most common data scientist skills:

Hard Skills for a Data Scientist Resume:

• Data Analysis
• Data Visualization
• Quantitative Analysis
• Machine Learning
• Mathematics
• Probability
• Programming

Soft Skills for a Data Scientist Resume:

• Critical Thinking
• Communication
• Time-Management
• Collaboration
• Data scientists frequently use tools, such as Cloudera, PERL, and OpenRefine. If there are any tools or pieces of software that you’re an expert in, include them in your skills section.

Here’s a more comprehensive list of 101+ must-have skills this year .

What Else Can You Include in a Data Scientist Resume?

We’ve now covered every essential resume section .

Is it the absolute BEST it can be?

Doing a great job with the above sections should be enough to get you shortlisted, but adding a few of the following sections could be the major factor in whether you become their new data scientist or not.

Awards & Certifications

Have you won an award for your work in a field that relates to data science?

Have you completed any courses to improve your skills and knowledge?

If you said yes to any of the above, make sure to mention them in your resume!

Don’t worry if you don’t have any awards or certificates, there a few companies that allow users to do online certifications, like Google.

• “IBM Data Science” - Coursera Certificate
• Google Certified Professional Data Engineer – GCP
• Microsoft Professional Program Certificate in Data Science
• “Deep Learning” - Coursera Certificate
• “Critical Thinking Masterclass” - MadeUpUniversity

Even though it is very unlikely to need a second language, you may want to add a small languages section to your resume. 

You see, being able to speak a second language is always an impressive skill to a hiring manager. 

Rank the languages by proficiency:

• Intermediate

Now, you may be wondering, “why would a recruiter need to know about my love for kayaking?”

Well, your hobbies reveal more about who you are as a person.

A hobbies section is an easy way to add personality to your resume, so add one if you have the space.

Here’s which hobbies & interests you may want to mention.

Include a Cover Letter with Your Resume

Here the thing –

Cover letters still play an important role during the application process.

They provide a number of benefits, but the main reason for using a cover letter is to show the recruiter that you care about working for their company.

To create a winning cover letter, we must use the correct structure. 

Here’s what we recommend:

You should complete the following sections:

Personal Contact Information

Your full name, profession, email, phone number, location, and website (or Behance / Dribble).

Hiring Manager’s Contact Information

Full name, position, location, email.

Opening Paragraph

It’s no secret that hiring managers skim through resumes and cover letters. As such, you need to hook the reader within the first few sentences. Use concise language to mention:

• The position you’re applying for
• Your experience summary and best achievement to date

Once you’ve sparked the reader’s interest, you can get deeper into the following specifics:

• Why you chose this specific company
• What you already know about the company
• How your skills relevant for the role
• Which similar industries or positions have you worked in before

Closing Paragraph

Don’t just end the conversation abruptly, you should:

• Conclude the points made in the body paragraph
• Thank the hiring manager for the opportunity
• Finish with a call to action. This is a good way to start a conversation. A simple “At your earliest opportunity, I’d love to discuss more about how I can help company X” will work

Formal Salutations

End the cover letter in a professional manner. Something like “Kind regards” or “Sincerely” will be proficient.

For more inspiration, read our step-by-step guide on how to write a cover letter .

Key Takeaways

If you followed all of the above advice, you’ve given yourself the best possible chance of landing that data scientist role.

Let’s quickly summarize what we’ve learnt:

• Format your data scientist resume correctly by prioritizing the reverse-chronological format and then following the content layout guidelines
• Start your resume with a summary or objective to hook the recruiter
• In your work experience section, give attention to your best achievements, rather than your responsibilities
• Craft a convincing cover letter for an unbeatable application

Suggested Reading:

• How to Ace Interviews with the STAR Method [9+ Examples]
• 22+ Strengths and Weaknesses for Job Interviews
• What Is Your Greatest Accomplishment? [3 Proven Answers]

To provide a safer experience, the best content and great communication, we use cookies. Learn how we use them for non-authenticated users.

• • Turned data into actionable insights, providing C-suite stakeholders with insightful recommendations to streamline business operations and improve customer experience.
• • Generated statistical reports and visualizations, providing key insights for more than 20 marketing campaigns and initiatives, including A/B testing, customer retention, brand awareness, and global expansion.
• • Worked with senior leadership to develop and implement digital marketing strategy, identifying and implementing new tactics to improve campaign performance by 50%, resulting in 250% increase in revenue from search marketing campaigns.
• • Designed and implemented a machine learning system that predicts hardware malfunction with more than 80% accuracy.
• • Created global and personalized real time reports system for executives stakeholders and processes in SAS, Tableau, and proprietary systems.
• • Worked closely with a team of data engineers and BI analysts to improve the efficiency customer recommendation analytics engine by 33%.
• • Collected technical requirements for $500K+ customer accounts, defining data rules for and KPIs for performance metrics.
• • Perform HR data collection and a variety of statistical analyses using Microsoft Excel, SAS, Tableau and Python.
• • Assisted senior data science team in building innovative machine learning models and segmentations for personalization initiatives to drive margin, revenue, and conversion.

14 Data Scientist Resume Examples & Guide for 2024

Customize this resume with ease using our seamless online resume builder.

All resume examples in this guide

Data Science Intern

Entry-Level Data Scientist

Senior Data Scientist

Machine Learning

Python Data Scientist

Associate Data Scientist

Data Science Manager

NLP Data Scientist

Metadata Scientist

Educational Data Scientist

Data Science Director

Data Science Consultant

Data Analytics Scientist

Senior Data Scientist | CAP | DASCA resume example

Resume Guide

Data Scientist Resume Example

Resume Format

Resume Experience Section

Hard & Soft Skills

Data Science Certifications

Resume Summary/Objective

Other Resume Sections

Key Takeaways

By Experience

Data science is a complex industry, and continues to evolve in today’s technological landscape.

Machine learning and ChatGPT may be booming right now, but it can be challenging to stay on top of these rapidly changing technologies.

Your data scientist resume needs to demonstrate your technical skills as well as your ability to communicate with others. Show the distinct value of each of your projects while avoiding redundancy.

Don’t worry, our guide will show you how to write an incredible data scientist resume that highlights your expertise in Python and SAS without overshadowing your interpersonal skills.

This guide will teach you:

• How to use our data scientist resume templates to make a good impression and attract recruiters’ attention.
• How to format your experience section so that hiring managers can see how you’ll impact success at their company
• How to showcase your skills in a way that shows you’re on top of industry trends and are the right candidate for your target job
• What recruiters look for and how to write a strong data scientist resume summary that gets callbacks

Looking for related resumes?

• Data Engineer Resumes ;
• Entry Level Data Analyst Resumes ;
• Tech Resumes ;
• SQL Developer Resumes ;
• Tableau Developer Resumes .

Data scientist resume example

How to format a data scientist resume

There are a few different resume formats to consider for your data scientist resume, but your best bet is to go with a reverse-chronological resume .

The focus of this format is on your work history listed in reverse-chronological order, just as the name suggests.

This is the best choice for senior data scientists who have been in the industry for 10 years or more. It brings attention to your career growth and shows your commitment to your work.

If you’re changing careers or just starting out as an entry-level candidate, a better choice is a functional resume format. This brings attention to your skills and away from your lack of experience.

When choosing a file format for your data scientist resume, always go with PDF. It provides extra security and ensures there won’t be any unwanted formatting changes.

Only use a DOC or another file format if the job application instructions explicitly say so.

As far as data scientist resume length, limit yours to one page. Recruiters don’t want to spend time reading through lengthy resumes, so stick to what’s relevant.

If hiring managers want to see more of your work, your GitHub link is there to guide them.

(CTA to ats checker - Enhancv to do)

The top sections on a data scientist resume:

What recruiters want to see on your data scientist resume:, how to create an impactful data scientist experience resume section.

The experience section is the core of your data scientist resume. It’s where you’ll let all your hard work shine.

To make the most impact possible, follow these key rules:

• Include only major and relevant positions - the 2-month stint behind the counter at your grandfather’s banana stand interests no one. But that job as a data engineer working on sales data for a national fruit reseller is something the recruiter needs to see!
• Make it reverse-chronological - it’s the resume standard, and it saves mental energy for the recruiter. List your most recent positions first.
• Focus on impact rather than responsibilities - data mining, statistical analysis, and data visualization will be on almost every data scientist’s resume. Instead, explain the impact you had rather than just listing job duties.

Let’s take a look at a data scientist resume experience section to see how to avoid a common mistake.

• • Created and presented models for loan success factors.
• • Did database manipulation of the Financial Aid Database.
• • Coordinated a team of data scientists.

What doesn’t work in this example:

• No quantitative metrics or measurable results
• Uses broad verbs like “did” and “coordinated” that don’t speak to success
• Leaves out industry-specific knowledge or skills

Let’s look at that example again with a few changes.

• • Designed and implemented models for loan success factors, achieving a 20% improvement of approval decision time.
• • Spearheaded complete database restructuring of the Financial Aid Database used across 16 different countries.
• • Coordinated a team of 20 data scientists working on 6 different projects for insurance, finance, marketing, and security departments.

What works in this example:

• Shows evidence of specific results by “achieving a 20% improvement of approval decision time”
• Shows project management skills by mention “team of 20 data scientists working on 6 different projects”
• Shows industry-specific “data restructuring” skills and reach of “16 different countries”

This version is a big improvement. It quantifies impact with measurable results and industry-specific skills.

Always focus on relevant achievements instead of general responsibilities and tailor every section of your resume to fit your target job.

How to quantify impact on your data scientist resume

Companies hire data scientists to provide solutions and maximize success. If you want hiring managers to give you a chance, you need to quantify impact on your resume.

Recruiters will be looking through a stack of resumes that all list “data visualization” and “algorithm development” as skills. It’s not enough just to list it. You need to prove it.

Provide evidence to support your claims by sharing specific achievements with measurable success. Use real data and numbers to quantify impact in every section of your resume.

Quantitative data that can strengthen your data scientist resume include:

• Increased sales revenue
• Reduced redundancy or errors
• Rate of engagement or number of users
• Improved algorithm accuracy
• Profit margin
• Time saved for the company
• ROI for projects

Use these metrics throughout your resume to show potential employers exactly how you’ve achieved succes in previous roles.

Writing an entry-level data science resume

Just because you’re a recent grad looking for your first job in data science, don’t start thinking “I’m done, I don’t have any experience yet!”.

You’re mistaken if you think you don’t have any experience. Consider including

• Course projects that involved data science work - surely you’ve practiced your skills on a few practical exercises you can list here. Just make sure you feature the new and exciting projects - no one wants to see the same tired Titanic Survivor project!
• Internships - no matter if it’s your uncle’s company or a university help gig, you probably learned a lot, including keeping up with deadlines, working well with others, and communicating data results to different audiences. Practical skills matter, even if they’re soft skills.
• Volunteer work or side projects - if you don’t have practical experience, create some. There are tons of local SaaS startups that would benefit from logistic regression analysis to uncover their user activation points - help them out and use that as a practical example in your resume.

As you can see, there is a lot going on beyond traditional 9-to-5 steady job experience. And all of these will look great on your data scientist resume!

Looking to build your own entry-level job resume? Follow the steps in our guide on How To Write Your First Job Resume .

How to list your hard skills and soft skills on your resume

A data scientist needs a unique set of skills that lets you explore, transform, visualize and model datasets, and also communicate constantly with diverse stakeholder groups.

Make a good impression by showing that you have the right combination of hard skills and soft skills to accomplish this.

In “ Top 10 Big Data Skills to Get Big Data Jobs ” Amit Verma presents a comprehensive list of languages and systems data scientists should be able to work with, including

Top data scientist technical skills

Make sure you include only things that you know well enough to start working with tomorrow. There’s no point in inflating expectations and then missing the mark.

What about soft skills?

Just knowing the technology won’t cut it, you need soft skills too. We list some great ones below, and you can check out KDnuggets list of important soft skills .

Data scientist soft skill examples

The world of data is complex. Demonstrate that you can navigate through it, but also help others orient themselves in it. Make sure you cover this, especially for more senior positions where presenting to managers is everyday work.

How to list your certifications and education on your resume

You’ve come a long way to becoming a data scientist. You’ve put in a ton of hours reading O’Reilly textbooks, debugging Python scripts, and creating visualizations in Tableau.

Make all your hard work show on your resume. For a stellar education section, add info on

• Your university and major
• Your GPA and final marks
• Key courses relevant to the position you’re applying for
• Any awards you received or societies you were part of

Since data science is a relatively new field, it’s common for professionals to come into it from different fields. If this is the case for you, you can shorten your education section and include additional courses and certifications you’ve earned.

Top 20 data scientist certifications you can take:

Make sure you follow a few rules when presenting certifications on your resume:

• Make them stand out - don’t bury your certifications in another resume section, give them their own
• Add any capstone projects you worked on - certifications usually make you show what you learned in practice, prove that you can do what you say
• Show them your drive - if you completed the certification course quickly, mention it on your resume. It shows dedication and motivation to learn.

How to write your data scientist resume summary or objective

You may have heard the terms summary and objective used interchangeably when talking about resumes.

To get specific, a summary typically captures your industry experience and a few career highlights in 2-3 sentences. An objective talks about what you want to achieve in the future.

These days they are usually combined into one statement and referred to by either name.

A good formula for your data scientists resume summary is to write 2-3 sentences that cover the following points:

• Your title and role in the industry
• A top career highlight
• A shared goal of you and your potential employer

Let’s look at an example that uses this template.

• Specific number of years of experience and industry focus
• Shares a measurable result that achieved “94% accuracy”
• States shared goal to “increase engagement with Python modules” with target employer

Additional sections for a data scientist resume

Depending on your experience and career path, there may be additional sections you want to include on your resume.

• Projects - including a section for projects can be key in increasing the value of a data scientist resume. Potential employers want to know how you’ve used your practical skills, and a successful project is a great way to show that.
• Awards - important industry achievements or competitive awards can be a great way to show your value. Include any relevant awards you’ve earned in the field.
• Volunteer work or hobbies - not all practical experience has to come from a job. There are plenty of ways you can develop relevant skills through volunteering or hobbies. Include any experiences that speak to your industry knowledge.
• Publications - a good data scientist is a clear communicator as well as a numbers person. Publications will highlight your ability to clearly communicate complex ideas.

Remember that publications aren’t just research papers published in peer-reviewed journals. This section can also include links to blog posts you’ve written to show that you can speak in more than just an academic tone.

Key takeaways for writing a competitive data scientist resume

To sum it all up, a great data scientist resume should tick these boxes:

• Make a good impression and show your body of work with links to your GitHub portfolio and LinkedIn profile
• Demonstrate practical knowledge and quantify impact with measurable results so hiring managers will know that you can achieve success
• Show how your skills align with the requirements in the job description by tailoring every section of your data scientist resume to your target job
• Include additional sections that show that you stay on top of industry trends and are the right candidate for the job

Now you’re ready to create your amazing data scientist resume and land an interview for your next job!

Data Scientist resume examples

Explore additional data scientist resume samples and guides and see what works for your level of experience or role.

Looking to build your own Data Scientist resume?

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Data Scientist Resume: Elements, Examples, and Tips

To write an effective data scientist resume, start with a solid understanding of what the role requires, along with the must-have elements. Learn what to include and how to format your resume with these tips.

Data scientists are responsible for some of the most impactful insights that organizations use to guide their decision making. To land such an important position, then, it's necessary that you make a first good impression using your resume.

In this guide, you’ll find helpful tips on writing your resume as a data scientist. We'll explain the education, experience, and skills you should include, as well as discuss job opportunities for data scientists and online courses that may prepare you for your next position.

What is a data scientist?

A data scientist works with data to answer questions and solve problems for a team, company, or organization. Data scientists gather, analyze, process, and model data and interpret the results. Data can be structured, such as dates, credit card numbers, and names or unstructured, such as social media posts, audio files, or surveillance video.

Many data scientists use computer science, social science, and math skills as they work to uncover trends and create solutions. They are both discoverers and problem-solvers. 

Read more: Data Science Jobs: Resources and Career Guide

Data scientist resume: elements and examples

To stand out to employers, your data science resume should be properly formatted and include an overview of your relevant work experience, education, skills, and certifications. Here's what you need to know about each of these different resume elements:

1. Formatting

Your data scientist resume should include the following sections: a header with your name and contact information, summary , work experience, skills, and education. It can also be appropriate to sprinkle in achievements, awards, or professional interests as room allows. Format your resume so that the focus is on those core elements.

When choosing a format, consider using reverse chronological order . This will show potential employers your most recent work history and education. Those items will often be the most relevant information, and a reverse chronological order format makes it easy to find these details. 

Read more: Resume Sections: How to Organize Your Resume

2. Work experience.

Your experience should be the first thing an employer sees after your contact information and an objective or brief summary. It’s a good idea to jump right to experience because, in the field of data science, experience carries a lot of weight. 

When listing your experiences, list them chronologically from your current job or most recent job, back. List relevant experience only. For each position you have held, organize the following pertinent information in this order:

Employer's name

City and state where the employer is located

Your start and end dates for the job (or "present" if you currently hold the position)

A bulleted list of your most noteworthy achievements and key responsibilities

If you’re a new graduate with no experience, emphasize the data-oriented projects you've worked on in your academic career or in your personal life. For example, you might note the following experiences:

The duties you performed in a data science internship or as a freelancer

Contributing to open-source projects like GitHub to build your portfolio 

Creating mock projects that you can link to show your skills 

Read more: How to Land a Data Science Internship: Your 2024 Guide

3. Education. 

List your education on your resume, with the most advanced degree listed first. If you don’t have a relevant degree in the field of data science, list your high school education.

You should list your education after your work experience section, unless you're a recent college graduate, in which case you should highlight your degree before your work experience. The reason for this shift is simple: recent graduates lack work experience but possess the training necessary to perform many data science tasks, so by putting their degree first they're emphasizing they have the required skills – even if they lack professional work experience.

To properly format your education history, follow this structure:

Degree type, major

Name of your school 

Years studied

Any relevant honors received 

Relevant coursework

Here’s a data scientist resume example of how you might list an education entry: 

Bachelor of Science in Statistics

University of Georgia

2012 - 2016

Relevant Courses: Probability and Statistics, Generalized Linear Models, Applied Statistics

You can abbreviate your degree (i.e. "B.S") or write it out in full if you have the space (i.e. "Bachelor's of Science"); either is appropriate for a data scientist resume. 

3. Describe your skills.

After listing work experience and education, it’s time to note your skills. List skills in bulleted format for easier readability, and use action verbs when possible.

Include your technical skills, beginning with those you feel are your strongest data science skills as related to the position you're applying for. You’ll want to list both technical and workplace skills . You don’t necessarily need to set them apart but mention both types of skills if they're relevant for the position. 

Read more: How to Feature and Format Key Skills on Your Resume

If you’re unsure which skills to list or what skills are irrelevant, refer to the job description for which you’re applying (or find a sample one online) and match your skills with the ones required for the position or a similar one.

4. List your certifications.

List any certificates you hold that are crucial to the job you're applying for to emphasize your preparedness for the role. This will highlight these essential skills in a way the hiring manager can easily notice.

In your role, you will likely obtain certifications in various programming languages, such as Python , SQL , and MySQL. You might also seek certification specific to the data scientist career field, such as SAS Certified Data Scientist or  Microsoft Certified: Azure Data Scientist Associate. If you have certifications but they are not pertinent to the job yet show you have additional skills that may be helpful to the position, list them under your education section.  

Format your certifications in the following way:

Full title of certification and acronym

Name of the organization from which you received the certification

Date you earned the certification 

Read more: Your Guide to Data Science Certifications in 2024

Data scientist resume tips

Most potential employers only spend a handful of seconds reviewing resumes, so make sure yours stands out. To help craft an eye-catching resume, consider the following tips:

1. Consider the employers’ viewpoint 

Highlight the skills that your employer notes in their job description. Consider common traits employers look for in a data scientist, as well. According to leaders in the industry, these are some of the top skills and attributes employers seek: 

Critical thinking  

Mathematical skills 

Machine learning

Data architecture 

Problem-solving 

Communication skills 

Remember to list both technical and interpersonal skills so that employers can get a well-rounded picture of who you are as an employee and a data scientist. 

If you want to really stand out to employees, consider enrolling in a data scientist certification that can be added to your resume. Certifications show employers that you are hard-working and serious about what you want to do in your career. 

Looking to develop job-relevant skills?

C onsider courses offered on Coursera like Machine Learning and Python for Everybody . These are both in-demand skills for any computer scientist and can give you an edge over the competition on your resume. 

2. Create an elevator pitch

An elevator pitch is a short, persuasive summary of why someone should hire you. As you gather your information to write your resume, take the time to jot out a short elevator pitch. This simple exercise will help you identify what’s important and relevant to potential employers. 

On your resume, take a few sentences from this pitch to communicate why you are the right person for the job over others. Describe what makes you unique in the field of data science. Highlight those skills and accomplishments that are most relevant to the position for which you’re applying. What makes you the best candidate for this position? 

3. Tailor your resume to each job

Remember, the purpose of a resume is to land an interview. Be selective with what you include. 

You should generally keep your data scientist resume length to one page if you are a student or recent graduate and two pages if you have experience.

Be concise in your descriptions and include only relevant information. Think about the things that can catch the attention of the employer. Read about the employer, do your research. This act alone can help you know what to highlight and what’s probably not important to them. 

And remember, in data science, projects and work experience are important. Pour your attention into these aspects of your resume. 

4. Keep your format simple

The goal is to create a document that can easily be skimmed through within seconds. Pay attention to whitespace, use bullet points, bold words for emphasis, and break up any large chunks of text. 

A good resume should be clean and easy to read. Avoid designs and a lot of “extras.” It’s important to include proper headers, consistent formatting (i.e., the same font throughout), and some white space. 

Read more: Types of Resumes: Choosing the Right Format For Your Needs

Build data science skills on Coursera

Stand out to potential employers with a certification from industry leaders on Coursera. In IBM's Data Science Professional Certificate , you'll learn the tools, languages, and libraries used by professional data scientists, including Python and SQL. Develop in-demand skills and hands-on experience to get job-ready in as little as five months.

Keep reading

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Editorial Team

Coursera’s editorial team is comprised of highly experienced professional editors, writers, and fact...

This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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