data analyst take home assignment example

Data Analysis Journal

How To Pass A Take-Home Python Assignment - Issue 59

A case study challenge for a senior analyst position at one of the biggest bay area companies completed in python.

Did you receive a take-home assignment? My condolences. Get on stack overflow or bookmark this issue.

Did they ask you not to spend more than 2-4 hours on a challenge? Haha. Very funny. But this is data analysis, not a comedy club. Let’s get down to business!

Let me do a proper intro. This publication is a successful example of a take-home assignment for …

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Six Steps to Pass the Data Science Take Home Challenge

How many times have you seen these exact words? Hopefully not much in your data science interview process. But if you’re one of many data scientists looking for a job, you might find yourself working on a data science take-home assignment in a zipped file with a requirements pdf that’s ten pages long.

The recruiter promises that there’s an intricate grading process on your assignment that shouldn’t take more than a couple of hours. But suddenly it’s 2:30 AM, three days later, 15 hours of coding exhaustion put in, and you haven’t even thought about trying that GAM model to see it improve your model’s F1 score by three percent. Why is this sort of thing continuing to happen? Why are companies wasting the candidate’s time still without any sort of feedback on the take-home?

The truth is that the process works as a filter for many companies that don’t have a standardized interview process. Development of technical interview questions requires a history of knowledge on the part of data science teams that just does not exist yet compared to software engineers.

So if you must, have to, and there’s no other choice, to do a take-home assignment. Here are a few steps to take to ensure a smoother process.

• Understand expectations
• State assumptions everywhere
• Do the modeling basics
• Make the take-home challenge readable
• Write Tests and Comments
• Summarize your thought process

1. Understand Expectations

It’s difficult to push against a company who is interviewing you or going to be interviewing you. But understanding the full expectations of the data science take-home challenge will be the key to passing it successfully.

Here’s an email template to use with the recruiter.

Hi Recruiter’s Name,

Thanks for sending over the take-home assignment. I’m excited to start it and will be sure to send it back in X days with my completed solution.

Additionally, I was wondering if I could be provided with a set of general guidelines on how the assignment will be graded. I definitely want to be sure I’m focusing and demonstrating the correct skillset for the take-home and not accidentally going down a rabbit hole.

Lastly, I would really appreciate it if after I send in my take-home assignment that I could get some feedback on it, regardless of whether or not I move on in the interview process. It would really mean a lot to understand what I did wrong or where I excelled for my own technical growth.

2. State Assumptions Everywhere

Try to immediately tally up a list of questions that you can send to the recruiter/hiring manager after receiving the take-home challenge. Even after getting answers to your questions or receiving no answer, make sure to then state your assumptions in your data science take-home challenge. What do I mean by that?

What if you decide to only use a naive imputation model to fill in missing values instead of an advanced technique? State it. Write it in a comment. Do something where they understand your limitations to the amount of time you’re spending on the assignment.

Write up everything that you think needs to be known to your grader. Hiring managers forgot how long it took to write code and build models. They’re managers. They don’t write code.

3. Do the Modeling Basics

Here’s a general checklist that will probably take you at least a minimum of three hours.

• Data cleaning
• Minimal feature selection
• Impute missing values
• Create a classification pipeline
• Try training with a couple of sci-kit learn classifiers
• Tune hyperparameters with grid-search

Boom. Now your implementation will reach the general minimal baseline of what they’re expecting. Dependent on how long you work on feature selection, it could go plus or minus an extra two to three hours.

4. Make the Take-Home Challenge Readable

Here’s a great guide toward code organization and readability for data scientists. It’s about structuring your project in an easy-to-digestible manner. I stumbled upon this randomly, but it completely makes sense. The Cookiecutter data science framework allows for a standardized process for data science projects. Taken directly from their website:

• Collaborate more easily with you on this analysis
• Learn from your analysis about the process and the domain
• Feel confident in the conclusions at which the analysis arrives

I will note that it will definitely take you more than a few hours to organize your project with the complete format. But then again you already understood the cost when you decided to do a data science take-home assignment.

5. Write Tests and Comments

Did I mention documenting everything in your head onto paper? That includes writing comments and testing your code if it’s applicable. Readability is as important as the efficiency of your code and if you write nice comment blocks on each function, it will help communicate how your code should function and why you re-factored it the way you did. Follow the general Python conventions to make sure you’re solid.

6. Summarize Your Thought Process

Remember in high school English when all papers consisted of an introduction, content, and then conclusion, which repeated the introduction? Do that but in under 500 words. At the end of the day, the most likely scenario is that the person looking at your take-home assignment will spend a grand total of five minutes of their time understanding it before moving on back to browsing Reddit. You want to make it as easy as possible for them to understand your data science take-home challenge as being the best possible take-home challenge ever.

How to Apply for a Data Science Job and How to Prepare for Interviews

Peter Scobas

• April 12, 2021

You have an awesome resume, a jaw-dropping portfolio, and a Pulitzer Prize winning cover letter. What’s next? Now we’re getting to the good stuff: applying for a data science position and preparing for the job interview(s).

For most junior data positions, the application process is relatively straightforward: after submitting your application, the recruiter will reach out and you’ll generally have a phone screen with the recruiter. Then, you’ll often be asked to complete a data science task as part of the technical screen. If you pass that, you’ll usually have a more technical conversation with the hiring manager, followed by the final stage: the onsite interview.

But let me show you the details.

In this article, I’ll discuss the following:

Where to look for job postings

How to follow up after submitting your application.

• How to prepare for the phone screen with the recruiter
• How to impress the hiring team with the technical data assignment

Before we get started…

This article is part of a five article series called: How to get a job in data science and analytics .

Here are all the articles:

• epsiode #1 — Intro: What is a data analyst/scientist and what skills do you need?
• episode #2 — What do you need to do before you apply? (resume/cover letter/website/GitHub help)
• episode #3 — How to apply and how to prepare for data science job interviews and how to ace the take-home assignment — this article
• episode #4 — Common junior data science job interview questions and how to answer them
• episode #5 — How do you negotiate? Should you negotiate? What is the career trajectory for someone in data science and analytics?

Now, it’s time to get into it:

This will be a pretty short section.

The best places to look for job postings are:

• Glassdoor ,
• AngelList , and

… generally in that order.

Note 1: if you end up using AngelList, you will need to put together a profile similar to LinkedIn. There isn’t a magical one-stop shop for all listings, but if you scour these sites you will definitely see the bulk of the job postings in data science and analytics.

Note by Tomi Mester: The above list applies to the US job market. When we are talking about Europe – in my experience – LinkedIn is pretty much the only go-to place when looking for data science positions. Of course, you can always extend your opportunities relying on your personal network. And direct applications through the website of a favorite company of yours is also possible (although it’s not common).

I consider the advice in this section somewhat optional. I would not do this for all the jobs you apply to, but following up for the select few jobs you are really interested in can make a difference.

Okay. So you submitted your application but you haven’t heard anything back. For job openings you are particularly interested in, I would do the following:

• Find the hiring manager’s email. This is generally pretty easy—you’ll just have to do some digging. You can find emails on LinkedIn, or use something like hunter . If you can’t find the hiring manager’s email, try to find someone on the data team.
• Once you have the email address, write a thoughtful email and attach your resume. Keep the email short, but interesting. Say what job you recently applied for, what you like about the company, how you believe you can make an impact, and end with a soft ask. Something like:
• Continue to follow up until you hear a response.

Alternative follow-up strategy

Another strategy that works is to message another data analyst/scientist on the team (either email or on LinkedIn), and say something like:

I would recommend you send this email out before you apply—if they never respond, apply to the job anyway. However, if they do respond, there’s a good chance you can give your resume to them and they can forward your resume to the recruiter.

I particularly like this strategy—for a few reasons. One, most people are nice—and it’s hard to turn down free coffee (quick note: buy their coffee for them). If/when you meet, keep the conversation light; ask them about their experience, what advice they have for you, and what they like about their company.

How to prepare for a phone screen interview with the recruiter

This stage of the application process — the phone screen — is also always pretty straightforward. The recruiter at this point is trying to check a few things:

• You can clearly speak about the stuff on your resume —the recruiter might ask you to explain a project you worked on that used data.
• The recruiter will most likely ask you what your programming background is—generally, they are looking for keywords like SQL , Python packages like maplotlib , Pandas , and NumPy, etc.
• Always have a few reasons for why you are leaving your current job and why you’re interested in this position/company (in a later section, I’ll go into more detail about how to answer some standard interview questions).
• Also, always have a few questions to ask the recruiter as well! Should be common sense, but definitely something that is overlooked when preparing for recruiter phone screens.
• The recruiter might ask about what salary range you’re interested in—see a later section on how to answer the salary range question.
• Be friendly and sound interested and excited. Recruiters are also trying to gauge if you’d be a “good fit.” It’s easier to check off this “good fit” box when you’re friendly and having an engaging conversation with the recruiter.

Remember: the recruiter probably does not know much about the position you’re applying for—meaning, they’re not going to ask you technical statistics questions, or questions about SQL/Python/R. They might have a list of questions they’re given to ask, like “have you worked with window functions?” or “what type of statistical modeling techniques have you used?” but the point of these is to make sure you have the skills the hiring manager is looking for.

Lastly, have an open (email) dialogue with the recruiter. If they set up a time to have you talk to the hiring manager, or come in for an onsite interview, ask them if they have any advice or anything that might be helpful to prepare for. As the application process goes into the later and later stages, recruiters want to get positions filled, and there is a good chance they will offer at least some advice. The more information you can have at each step of the process, the better.

How to impress the hiring team with the take-home data assignment

Oh, the famed take-home data task. In this section, I have a list of DOs and DON’Ts for you, as well as a conceptual discussion on how to ace the take-home assignments. (These are based on real assignments from some of the biggest tech companies.) I’ll also show you how I would approach the questions, how I would approach writing the code, and how I would present my results.

In this technical screening step, there are usually two types of tasks:

• Timed programming challenge . I do not have much advice on how to prepare for these. Most companies (that I know of) do not use this as a step in the application process, but if you find yourself having to complete one of these, just do your best and don’t stress about finishing in the allotted time—just make sure what you can finish is correct and a good representation of your programming skills.
• Data analysis challenge . This is the most frequently used technical screen. It usually involves a dataset and some questions to show off your programming skills as well as your ability to analyze and synthesize results. This section is focused on this type of technical challenge.

The data analysis challenge is used to evaluate the following:

• Can you demonstrate the technical skills you discussed in your resume?
• Are you able to handle and clean messy data?
• Is your code clean, well-written, and well-documented?
• Are you able to clearly communicate and present your results?

This data assignment phase is where you can initially stand out in the interview process.

If you follow my advice, you can set yourself apart from the other applicants with a strong data assignment. Other than the resume/cover letter step, this is the one phase of the application process you have complete control over. You can generally spend as much time on it as you want, troubleshoot your code, look at resources online if you need to, and think through how you want to approach the task and how you want to present your work.

DOs and DON’Ts for the data science take-home assignment

So with that said, here are some DOs and DON’Ts for how to be successful in the take-home assignment stage:

• DO : if you’re really interested in the job, finish the assignment. Not sure why this is the case—but a fair number of the assignments I review are incomplete. If you really want to make a strong impression, finish the assignment.
• DO : make it extremely easy for the team to review your assignment. In 99.99% of cases, all you need to send back are two files: one text-file of your code, and one PDF with the questions you had to answer in bold, and your answers/visualizations/results below.
• DON’T : send a Jupyter notebook, html file, etc. You run the risk of the formatting being off and it will look disjointed and clunky. Just think about this: if you get hired, your job would be to take a question, use data to answer that question, and present and/or communicate your findings to non-technical people. You’re not going to send someone on the marketing team a Jupyter notebook where they have to sift through the code.
• DON’T : send your code without a write-up of your results/findings. The hiring team will not replicate your results; they probably won’t even look at it. You’re wrong if you think people will spend the time debugging your code, making sure the directories are correct, all the packages are installed to run, etc. Again, you’re not going to send an executive a text file saying “make sure to install the following packages to review my findings.” So don’t do that in your data task.

The people looking at your assignments are interested in two things: clean code and clear storytelling ability —so make sure to demonstrate that in your assignments!

Examples of a junior data scientist’s take-home assignment

Now, I’m going to describe two database schemas that are similar to what might be given to you. I’ll discuss:

• potential questions you could be asked to answer using the data or questions you should consider answering if the prompt is vague,
• how you should approach answering those questions, and
• potential “Easter eggs” and things to look out for and consider while doing your data cleaning and analysis.

But first, let’s quickly discuss the two files to send once you’ve completed the data assignment: a text-file with your code, and a PDF of your analysis and results:

The text file you’ll send back

For the text-file, keep it simple, clean, and well-documented. Format it like the example for the personal project code ; see the screenshot below for a reminder:

The analysis PDF you’ll send back

For the PDF showing your analysis, keep it organized and easy for whoever is reviewing it to know which question you are trying to answer. See the screenshot below for an example of how to organize and format this page:

Mock Data Assignment #1

Alright, alright, alright. Now I’m going to walk you through the two mock data assignments. I’ll describe a dataset and discuss a few things to consider.

Let’s start with the first one.

(Note: there is no actual data in this section, just a conceptual exercise)

In most cases, the dataset(s) you are given require at least a little bit of data cleaning. This can be anything from converting variable types, dealing with unnecessary whitespaces, things like that. Not to make you paranoid, but it appears to be relatively common for companies to have a few “Easter eggs” in the data—either in the data cleaning stage or the analysis stage of the assignment. So, it is important to keep this in mind and be on the lookout for things like null or missing values, incorrectly spelled data, and certain IDs acting like “bots” that will screw with your results. When you find these types of things, make sure to document them either in your code comments or in your write-up.

Typical questions

Oftentimes, companies will ask super general questions in the data assignment to gauge what insights you can gather from the data; questions like:

• What trends do you see in the data?
• What insights from the data are actionable?
• If there are multiple datasets, how do the datasets differ?
• What are the most interesting findings?
• Can you use a model to predict X, Y, or Z?

Great answers

After cleaning and checking the above dataset for any errors, there are a number of “low-hanging fruit” visualizations you can put together:

• Bar chart showing number of customers by acquisition channel
• Histogram showing length of time it takes users to go from installing the application to creating an account. (The thinking here is that the user experience is like a funnel—and users go from acquisition channel to installation to account creation—a long installation to account creation time suggests that there is confusion or a pain-point for users.) Similarly, you can make a funnel showing what percent of users go from acquisition channel to installation to account creation
• Plot showing number of actions by day. You can also divide users into cohorts based on when they installed the application. Do different cohorts tend to behave differently?

Obviously, this list isn’t exhaustive. But it should give you a good idea of some simple visualizations you can put together to show companies that you can clean data and present it in an aesthetically-pleasing way.

You can also suggest using the data for prediction. For example, based on when and what action a user takes, can you predict when and what their next action will be?

And lastly—even if they don’t ask, it is also good practice to have a short section at the end with any questions or concerns you have about the data and possible avenues for further analysis. You want to make the most out of the opportunity to showcase that you are vigilant and concerned about data integrity (meaning, you make sure the data is correct/accurate/error-free), and you are thinking about other ways to analyze and glean insights from the data.

Mock Data Assignment #2

Note: there is no actual data in this section, just a conceptual exercise

Similar to the mock data assignment #1, here are a few visualization ideas:

• Histogram showing lengths of time users tend to stay on a page—what about segmenting this by content_genre or content_length?
• Bubble chart showing most popular daily content
• Donut chart showing site activity by device type

Can you cluster or segment users based on what content they consume? In addition, if this was a publishing platform, can you tag a user as an “entertainment” or “sports” reader? Does the length of the page title have any effect on how “popular” content is? Does it get more visits if the title is longer/shorter?

Try to push yourself to come up with at least one visualization that you know other applicants will most likely not have—you want to stand out and demonstrate your critical thinking and creativity. You want whoever is reviewing your assignment to think to themselves, “Huh, that’s pretty cool.”

Go on to the next episode!

So this is all you have to know about the application process for a data science position. We covered: where can you look for job postings? We talked about following up after submitting your application. You got some advice to prepare for the phone screen with the recruiter. And we also reviewed how to win the take-home assignment.

If you passed these rounds, the hiring process is not over. It’s time for the onsite interviews. And as of that, in the next article I’ll show you the most common junior data science job interview questions — and also how to answer them! Here.

• If you want to learn more about how to become a data scientist, take Tomi Mester’s 50-minute video course: How to Become a Data Scientist.  (It’s free!)
• Also check out the 6-week online course: The Junior Data Scientist’s First Month video course.

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The Junior Data Scientist's First Month

How to Prepare for a SQL Take-Home

The concepts to study, how to practice, and what to include.

You found a dream analytics engineering role on LinkedIn with a company whose mission you are extremely passionate about.

You submitted your resume and heard back from the recruiter. You passed the initial phone screening and even talked to the hiring manager. You can already picture yourself working on the data team of this company!

You are moved to the …

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My solution to the book A Collection of Data Science Take-Home Challenges

JifuZhao/DS-Take-Home

Folders and files, repository files navigation, ds-take-home.

Solution to the book "A Collection of Data Science Take-Home Challenges" .

Please don't contact me for the dataset.

This repository is only for self-learning purpose. I am really happy if my solution is helpful to you. However, I won't provide the original book or the data files. If you want to do the exercise, you can go to https://datamasked.com/ to purchase the book. Please respect the author of the original work.

• Conversion Rate
• Spanish Translation A/B Test
• Employee Retention
• Identifying Fraudulent Activities
• Funnel Analysis
• Pricing Test
• Marketing Email Campaign
• Song Challenge
• Clustering Grocery Items
• Credit Card Transactions
• User Referral Program
• Loan Granting
• Json City Similarities
• Optimization of Employee Shuttle Stops
• Diversity in the Workplace
• URL Parsing Challenge
• Engagement Test
• On-Line Video Challenge
• Subscription Retention Rate
• Ads Analysis

Other useful resource: https://github.com/stasi009/TakeHomeDataChallenges

Copyright @ Jifu Zhao 2018

• Jupyter Notebook 100.0%

4 Case Study Questions for Interviewing Data Analysts at a Startup

A good data analyst is one who has an absolute passion for data, he/she has a strong understanding of the business/product you are running, and will be always seeking meaningful insights to help the team make better decisions.

Jan 22, 2019 . 4 min read

• If you're an aspiring data professionals wanting to learn more about how the underlying data world works, check out: The Analytics Setup Guidebook
• Doing a case study as part of analytics interview? Check out: Nailing An Analytics Interview Case Study: 10 Practical Tips

At Holistics, we understand the value of data in making business decisions as a Business Intelligence (BI) platform, and hiring the right data team is one of the key elements to get you there.

To get hired for a tech product startup, we all know just doing reporting alone won't distinguish a potential data analyst, a good data analyst is one who has an absolute passion for data. He/she has a strong understanding of the business/product you are running, and will be always seeking meaningful insights to help the team make better decisions.

That's the reason why we usually look for these characteristics below when interviewing data analyst candidates:

• Ability to adapt to a new domain quickly
• Ability to work independently to investigate and mine for interesting insights
• Product and business growth Mindset Technical skills

In this article, I'll be sharing with you some of our case studies that reveal the potential of data analyst candidates we've hired in the last few months.

For a list of questions to ask, you can refer to this link: How to interview a data analyst candidate

1. Analyze a Dataset

• Give us top 5–10 interesting insights you could find from this dataset

Give them a dataset, and let them use your tool or any tools they are familiar with to analyze it.

Expectations

• Communication: The first thing they should do is ask the interviewers to clarify the dataset and the problems to be solved, instead of just jumping into answering the question right away.
• Strong industry knowledge, or an indication of how quickly they can adapt to a new domain.
• The insights here should not only be about charts, but also the explanation behind what we should investigate more of, or make decisions on.

Let's take a look at some insights from our data analyst's work exploring an e-commerce dataset.

2. Product Mindset

In a product startup, the data analyst must also have the ability to understand the product as well as measure the success of the product.

• How would you improve our feature X (Search/Login/Dashboard…) using data?
• Show effort for independent research, and declaring some assumptions on what makes a feature good/bad.
• Ask/create a user flow for the feature, listing down all the possible steps that users should take to achieve that result. Let them assume they can get all the data they want, and ask what they would measure and how they will make decisions from there.
• Provide data and current insights to understand how often users actually use the feature and assess how they evaluate if it's still worth working on.

3. Business Sense

Data analysts need to be responsible for not only Product, but also Sales, Marketing, Financial analyses and more as well. Hence, they must be able to quickly adapt to any business model or distribution strategy.

• How would you increase our conversion rate?
• How would you know if a customer will upgrade or churn?
• The candidate should ask the interviewer to clarify the information, e.g. How the company defines conversion rate?
• Identify data sources and stages of the funnels, what are the data sources we have and what others we need, how to collect and consolidate the data?
• Ability to extract the data into meaningful insights that can inform business decisions, the insights would differ depending on the business model (B2B, B2C, etc.) e.g. able to list down all the factors that could affect users subscriptions (B2B).
• Able to compare and benchmark performance with industry insights e.g able to tell what is the average conversion rate of e-commerce companies.

4. Metric-driven

• Top 3 metrics to define the success of this product, what, why and how would you choose?
• To answer this question, the candidates need to have basic domain knowledge of the industry or product as well as the understanding of the product's core value propositions.
• A good candidate would also ask for information on company strategy and vision.
• Depending on each product and industry, the key metrics would be different, e.g. Facebook - Daily active users (DAU), Number of users adding 7 friends in the first 10 days; Holistics - Number of reports created and viewed, Number of users invited during the trial period; Uber - Weekly Rides, First ride/passenger …

According to my experience, there are a lot of data analysts who are just familiar with doing reporting from requirements, while talented analysts are eager to understand the data deeply and produce meaningful insights to help their team make better decisions, and they are definitely the players you want to have in your A+ team.

Finding a great data analyst is not easy, technical skill is essential, however, mindset is even more important. Therefore, list down all you need from a data analyst, trust your gut and hiring the right person will be a super advantage for your startup.

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5 Examples of Take-Home Tasks for Different Roles

Assigning take-home tasks when hiring is much like marmite, coriander, or Hawaiian pizza. Your candidates will either love it or hate it.

The ‘love it’ camp likely welcomes the opportunity to showcase their skills and appreciate the time to think it through versus answering questions on the spot in an interview.

However, the ‘hate it’ group sees it as doing work for free, might already have portfolios of work that give a much fairer picture of their experience level, and resent the infringement on their personal time (regardless of how this might be their dream job).

What we can learn from this dichotomy is that while a take-home assignment is not right for every role, it’s still worth it for some. To figure out if it’s a fit for the role you’re hiring for, let’s look at five good examples of take-home tasks that your candidates will (hopefully) love.

TL;DR — Key Takeaways

A take-home assignment is an important part of the interview process that focuses on candidates crafting and completing real-world tasks .

Incorporating a take-home assignment will give your organization better insight and skill observation over candidates. However, job seekers may see take-home tests as time-consuming, exploitative, or manipulative.

The perfect take-home assignment should be structured around providing the candidate with clarity about the role, respecting their time, and ensuring consistent testing criteria.

Toggl Hire introduced homework tasks in our skills assessment library! It’s never been easier to raise the quality of your hires with reliable proof of competence.

What are take-home tasks?

A take-home assignment is given to candidates during the interview process to complete in their own time and shows the hiring manager how the job seeker is able to complete a task.

These assignments generally consist of coding tests for developers , presentations for upper-level management, and campaigns for marketers. They’re given to candidates after the first interview round. The success will determine if the candidate makes it to the second round.

Pros and cons of a take-home assignment

Obviously, there are pros and cons to using a take-home assignment, right? Of course! So let’s go over the big ones.

• Skill observation : It allows the hiring company to understand the candidate’s skills in action and their thought process.

• Insight : The take-home interview assignment will allow the candidate to have a better understanding of the position, break any key assumptions, and what the company expects of them.

• Supplemental information : If done early in the interview process, an interview assignment allows the candidate’s skills to do the talking as opposed to the hiring manager only relying on the resume.

• Less pressure : Because a Q&A interview can be a pressure cooker, the take-home assignment makes the interviewing candidate feel more at ease.

• Time-consuming : A hiring team may claim the assignment will only take several hours to prepare and complete, but any interview assignment over more than an hour is cutting into the candidate’s personal time and current job.

• Ethical concerns and lost earnings : Asking a candidate to complete an unpaid work assignment can be seen as unethical and equivalent to unpaid labor. Some companies may even go so far as to steal the ideas of the candidate, use them, and not give credit or compensate the candidate.

• Limited personal evaluation : While the interview take-home assignment can assess a candidate’s skill set, it may not capture important aspects such as personality and behavior.

How to structure a take-home task

Creating a take-home assignment that strikes the perfect balance of helpful but not exploitative can be tricky. Regardless of what kind of take-home task or homework assignment you’re creating for hiring, it’s crucial for hiring managers to approach their creation with careful thought and attention.

Your hiring team will need to consider all of the following:

What are the common mistakes?

It’s normal to make mistakes, and learning from them can help you hire better, faster, and more fair.

So, let’s explore common blunders to steer clear of when designing and implementing a take-home assignment during the interview process, ensuring fairness and an effective evaluation process that respects candidates’ time and effort.

• Appropriate Task Alignment : Avoid assigning tasks that aren’t directly relevant to the role.

• Reasonable Task Length : Create a take-home assignment that can be completed within a reasonable timeframe.

• Providing Sufficient Context : Avoid requesting candidates to answer or solve company-specific problems without providing adequate information.

• Ethical Treatment of Work Requests : Refrain from asking candidates to produce work for free that the company may later exploit, such as writing a blog post for publication.

• Timely Introduction of Tasks : Including a take-home assignment as an early screening requirement can discourage candidates. Do this after their first interview.

• Constructive Feedback : Don’t miss the opportunity to provide candidates with constructive feedback on their completed tasks.

• Balancing Mandatory and Optional Tasks : Avoid making the take-home assignment mandatory for all applicants, as circumstances may prevent some candidates from completing it.

• Conduct post-assignment interviews : Once you have received a few tasks back from candidates, we highly recommend that you schedule a take-home assignment interview to better understand any pain points the job seeker may have had.

5 thorough examples of great take-home assignments

Now that you better understand the how , the when , and the why of take-home assignments, we’ll show you five examples. The example take-home assignments will cover tasks for:

• Developer – fixing a broken site
• Product manager – redesigning a feature
• Marketing lead – creating a marketing campaign
• Designer – redesigning the onboarding flow
• Customer success executive – running a mock QBR

Example #1: Take-home task for a developer role

This challenge is geared towards a mid-level developer who can identify and fix errors and optimize the code of an eCommerce website. The goal here is to see how well the candidates understand debugging techniques, approach problem-solving, and how they will communicate with the rest of their team.

Task: Fixing a Broken E-commerce Site

Introduction

Your mission is to debug the broken e-commerce site, fix errors, and ensure it runs smoothly. Customers are unable to place orders due to the significant increase in errors.

Requirements

• Identify and fix all of the errors on the site.
• Ensure that customers can place orders without any problems.
• Optimize the site to improve its performance.
• Document your approach and explain your reasoning behind your changes.

Instructions

• Clone the repository from the following Github URL: https://github.com/debugging-challenge/e-commerce-site.git .
• Install all the dependencies by running npm install .
• Start the development server by running npm start .
• Debug and fix all errors.
• Document your approach and explain your reasoning in a README file.

Your submission will be evaluated based on the following criteria:

• Identification and fixing of all errors
• Site optimization
• Completeness of documentation and reasoning
• Code cleanliness and adherence to best practices
• Clarity and organization of documentation
• Submit your code as a ZIP file.
• Include the README file that explains your approach and reasoning.
• Send the ZIP file to the hiring manager by email.

Example #2: Challenge for a product manager

Our next example focuses on testing product manager candidates on how they approach problem-solving, communicate with customers, and conduct user research while implementing open-ended questions.

In a sense, how well they’ll actually do their jobs in a product management role. This assignment is bound to produce better product management interviews for your organization.

Task: Redesigning Filma’s Collaboration Features

You are the Product Manager for collaboration features at Filma, a leading collaborative design platform. Recent feedback from customers has shown that they are not happy with how collaboration features work on the site. Your mission in this product management task is to redesign the collaboration features to better meet customer needs and preferences.

• Review the problem statement and develop a list of open-ended questions to better understand the issue.
• Conduct user research to validate assumptions and identify pain points and user needs.
• Develop a new design for collaboration features.
• Prioritize features and functionality based on customer needs and business goals.
• Outline the implementation plan.
• Document your approach and explain your reasoning.
• Review the problem statement and develop a list of open-ended questions to better understand the issue and customer needs.
• Conduct (mock) user research to validate assumptions and identify pain points and user needs. Schedule a call with a team member to role-play a customer interview. Include data points such as user feedback, user behaviour, and competitor analysis in your research.
• Develop a new design for collaboration features. Identify the key features and functionality of the new design, and prioritize them based on customer needs and business goals.
• Outline the implementation plan. Include a timeline, resources required, and technical feasibility.
• Document your approach and explain your reasoning in a presentation or document.
• Quality of open-ended questions and user research.
• Soundness of the new design and prioritization of features and functionality.
• Clarity and feasibility of the product management implementation plan.
• Completeness of documentation and reasoning.
• Clarity and organization of presentation or document.
• Submit your open-ended questions, presentation, or document as a PDF or PowerPoint file.
• Send the file to the hiring manager by email.

Example #3: Testing marketing managers

Let’s now explore an exciting marketing challenge that aims to find a candidate who can skillfully design an innovative user acquisition growth loop. This task involves leveraging valuable market research insights to craft a robust strategy that showcases a deep understanding of growth concepts.

Task: Designing a User Acquisition Growth Loop

You are the Marketing Lead at a Product-Led Growth (PLG) company that provides a collaboration tool for remote teams. Your team has conducted market research to identify target customer segments. Your mission is to design a new user acquisition growth loop based on the insights gained.

• Review the market research insights provided by your team.
• Design a new user acquisition growth loop, with a structured approach, based on the insights gained.
• Identify metrics to measure the effectiveness of the growth loop.
• Review the market research insights provided by your team. Use the insights to identify areas where a new user acquisition growth loop can be designed.
• Design a new user acquisition growth loop based on the insights gained. The growth loop should identify key stages, such as awareness, interest, and activation, and prioritize them based on customer needs and business goals.
• Identify metrics to measure the effectiveness of the growth loop. The metrics should be tied to the key stages of the growth loop and should be used to track progress and optimize the loop over time.
• Soundness of the new user acquisition growth loop and prioritization of key stages
• Creativity and effectiveness of the growth loop design
• Identification and feasibility of metrics to measure the effectiveness of the growth loop
• Clarity and organization of presentation or document
• Submit your presentation or document as a PDF or PowerPoint file.

Example #4: Take-home test for designers

This challenge is centered around an intriguing product design assessment designed to narrow down a candidate who excels in analyzing user recording sessions and crafting an improved onboarding flow design.

Task: Redesigning the Onboarding Flow Introduction

You are a Product Designer at a web-based Product-Led Growth (PLG) company that provides a collaboration tool for remote teams. Your team has recorded user sessions for the past 3 months to help identify areas of improvement for the onboarding flow. Your mission is to redesign the onboarding flow to improve user engagement and activation based on the insights gathered.

• Analyze the user recording sessions to identify user needs and preferences.
• Develop a new design for the onboarding flow.
• Prioritize design features based on user needs and business goals.
• Ensure that the design aligns with the company’s minimalist, intuitive design philosophy.
• Analyze the user recording sessions to identify user needs and preferences. Use the insights gathered to identify areas for improvement in the onboarding flow.
• Develop a new design for the onboarding flow. Identify the key stages of the flow, and prioritize them based on user needs and business goals. Ensure that the design aligns with the company’s minimalist, intuitive design philosophy.
• Prioritize design features based on user needs and business goals. Identify the most important design features that will enhance user engagement and activation.
• Quality of analysis of user recording sessions and identification of user needs and preferences
• The soundness of the new onboarding flow design and prioritization of key stages
• Alignment with the company’s minimalist, intuitive design philosophy
• Creativity and effectiveness of the prioritized design features

Example #5: Testing customer succes

Our final challenge example focuses on a customer success assignment. The perfect candidate will showcase their expertise in defining success metrics for a simulated account, devising impactful tactics to drive feature adoption and enhance metrics, and effectively presenting their approach and results in a mock Quarterly Business Review (QBR) presentation.

Task: Driving Feature Adoption and Improving Metrics

You are a Customer Success Manager at a PLG company that provides a project management tool for remote teams. Your mission is to work with a mock account to define success metrics, develop tactics to drive feature adoption and improve metrics for Q2, culminating in a mock QBR presentation.

• Define success metrics for the mock account.
• Develop tactics to drive feature adoption and improve metrics.
• Document your approach and results in a mock QBR presentation.
• Define success metrics for the mock account. Assume that the mock account is a remote team of 20 people that uses your project management tool for all their projects. Assume that they have been using the tool for 6 months, and that they have expressed interest in increasing feature adoption and improving metrics related to on-time delivery, collaboration, and budget management. Use your own assumptions to define success metrics that measure the impact of the product on their business.
• Develop tactics to drive feature adoption and improve metrics. Use the success metrics to identify the actions needed to increase feature adoption and improve metrics, and assign responsibilities to your team. Use customer success best practices, such as regular check-ins and training sessions, to ensure that the tactics are on track and that the mock account is engaged and satisfied.
• Document your approach and results in a mock QBR presentation. Create a deck that’s less than 10 slides, with consistent title and object placement, fonts, font colors, and different ways of visualizing insights. Use the mock QBR presentation to realign on the mock account’s goals, review their performance, present the tactics and their impact on the success metrics, and recommend the next steps to improve product performance next quarter.
• Quality of success metrics defined for the mock account.
• Soundness of the tactics to drive feature adoption and improve metrics.
• Collaborative execution of the tactics with your team.
• Clarity, organization, and persuasiveness of the mock QBR presentation.
• Submit your mock QBR presentation as a PDF or PowerPoint file.

Try a Homework Assignment by Toggl Hire

Ready to add homework assignments to your hiring process? Our homework assessments provide invaluable insights for hiring managers evaluating candidates ‘ ability to solve job-specific assignments.

Designed to test the hands-on skills necessary for day-to-day work, these assessments offer a glimpse into a candidate’s potential future job performance . With over 500 pre-built tasks available in Toggl Hire’s library, you can quickly implement comprehensive tests that align with your hiring needs.

Toggl Hire’s homework assessments are highly flexible, allowing for either integration with other assessments or standalone use. Create your free account now to explore a few examples!

Juste loves investigating through writing. A copywriter by trade, she spent the last ten years in startups, telling stories and building marketing teams. She works at Toggl Hire and writes about how businesses can recruit really great people.

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