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7 types of Data Science interviews you should be prepared for

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Dawn Choo
June 11, 2024

7 types of Data Science interviews you should be prepared for

Product Data Science jobs are in high-demand. Despite the difficult job market, there are many Product Data Science openings right now. Meta, TikTok, Spotify and Coinbase… are among the many companies hiring for Product Data Scientists.

And the craziest part? Some of these Data Scientist offers are offering up to $500k in total compensation. 🤯

Over the years, I have interviewed for 20+ Product Data Science roles, and received offers from Meta, Google, Spotify, Airbnb to name a few. Here are the 7 types of interviews (and practice questions) you should expect to encounter.

Looking for sample answers & frameworks for these questions? Check out my Product Data Science Interview Guide here.

1. Product sense interview

These interviews are aimed at understanding how you would approach a real-world problem as a Data Scientist on a product team. They typically come in the form of a case question.

Practice question 1: What are some ways to increase user retention on an online grocery shopping app?

Practice question 2: You're part of the search team at an e-commerce company. The marketing team has noticed that the search results for certain product categories are not as relevant as they could be, leading to lower conversion rates. How would you approach this problem and improve the search relevance?

2. Metric definition interview

Metric definition questions are, as the name suggests, about defining metrics. For example, interviewers could ask you to come up with metrics to measure the success of a product feature, or to size the opportunity of a new market.

Practice question 1: You are the Data Scientist on Tinder, what metrics would you use to understand how usage is growing?

Practice question 2: Microsoft is considering building a free, online version of Excel. What metrics would you use to size this opportunity?

3. Metric investigation interview

Metric investigation questions are designed to assess your ability to get to the root cause of a metric movement, and to recommend next steps based on your findings.

Practice question 1: During a weekly report, you find that the average session duration on Candy Crush has suddenly increased by 50% in one month. Describe your process to determine where the change is coming from.

Practice question 2: Duolingo, a language-learning app, sees a 25% increase in daily active users but a decrease in lesson completion rates. How would you go about uncovering the factors influencing these trends?

4. Statistics interview

These interviews assess how you apply your statistical knowledge in a product setting. Check out this list of Statistical concepts you should know as a Product Data Scientist.

Practice question 1: Your company wants to estimate the average income of its users. You have randomly sample 1,000 users and find that the mean income is $50,000 with a standard deviation of $10,000. What is the 95% confidence interval for the true mean income of all users? How would you interpret this interval, and what assumptions are required for it to be valid?

Practice question 2: You're analyzing user behavior on YouTube. You notice that users who watch at least one video per week have a higher retention rate than those who don't.

5. Experimentation interview

These interviews are aimed at testing your understanding of experimentation principles, and how to apply it in a Product setting.

Practice question 1: What are some scenarios where you might avoid running an AB experiment?

Practice question 2: Your team is testing a new feature that aims to increase user engagement. The test results show a 5% increase in engagement, but the p-value is 0.07. The product manager wants to launch the feature, arguing that a 5% increase is significant. What would you recommend and why?

6. Coding interview

Don’t be fooled by the name of this interview. This isn’t just about your coding skills, but also an opportunity to demonstrate how you combine your data skills with business sense. These are typically done in SQL & Python.

Practice question 1: Given a table of user logins with columns for user ID and login date, write a query to identify the consecutive login dates for each user. Display the user ID, login date, and a flag indicating whether the login date is consecutive with the previous login date for that user.

Practice question 2: Using SQL or Python, segment our users into different tiers (e.g., bronze, silver, gold) based on their lifetime value (total revenue generated). How would you determine the threshold values for each tier?

7. Behavioral interview

Behavioral interviews focus on your past experiences and how you've handled specific situations. Be prepared to discuss your problem-solving approach, collaboration skills, and ability to learn from failures.

Practice question 1: Tell me about a time when you used data to convince a stakeholder to change their opinion.

Practice question 2: Describe a project where you had to work with a cross-functional team to achieve a goal.

Looking for sample answers & frameworks for these questions? Check out my Product Data Science Interview Guide here.

Thanks for reading and for sharing your most valuable resource with me, your time.

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