Meta Data Science Interview Guide [30 LEAKED Questions from 2024]

Meta is the best place in the world to be a Product Data Scientist. But, I’m a bit biased – I worked on Facebook’s Growth Team and wrote a book with my bestie whose an Ex-Facebook Data Scientist. We've got a lot of love for the company, and want to see you get hired there too!

In this article, we'll share insider tips into the Meta Product Analytics Data Science interview process, and leak share 30+ recently asked Meta Data Science interview questions. After you read this 6,000 word guide, you’ll be ready ace the Meta interview, just like we did:

In this Meta Data Science Interview guide, we’ll cover:

• Stages of the interview process
• Analytical Execution Questions
• Analytical Reasoning Questions
• Research Design Questions
• Product Goals and Success Metrics Questions
• Recently asked Meta SQL Interview Questions
• Product Case Study Questions
• Meta Behavioral Interview Questions
• 7 Resources to Prepare for the Meta Data Science Interview

The Meta Data Scientist (Product Analytics) Interview Process

The interview process for Meta usually takes about 4-6 weeks. During that time you will have multiple SQL, product-sense, and analytical case study rounds. Let's dive into each part of the interview loop:

Round 1: Recruiter Screening

The first step in the Meta interview process is the recruiter screen:

• 💼 Format: Phone Call
• ⏰ Duration: 30-45 minutes
• 👤 Interviewer: Technical Recruiter or Talent Acquisition Specialist
• ❓ Questions: Culture fit, Understanding your Experience, Logistics

Insider Tip: Have a convincing answer ready to go for the inevitable question "Why do you want to be a Product Data Scientist at Meta?". Your best bet is to tell a story about a time you did data work that and had to work closely with business & product stakeholders. Your story should try to use keywords like A/B testing, product analytics, SQL, because these are the skills a Meta recruiter is trained to listen for.

Note that Product Analytics Data Scientists at Meta DO NOT build Machine Learning models. So don't yap on-and-on about your passion for Deep Learning and PyTorch and training LLMs. You might be a great Data Scientist/Machine Learning at some other company, but a terrible fit for this specific role which is much more SQL & product-sense heavy.

Round 2: Technical Screening

The next step after the phone screen is a virtual technical screen:

• 💼 Format: Virtual video call
• ⏰ Duration: 45 - 60 minutes
• 👤 Interviewer: Hiring Manager/Senior Data Scientist
• ❓ Questions: Technical Skills (SQL), Product case

The Meta SQL test is typically conducted on Coderpad, or a similar virtual coding environment where the interviewer can watch you code.

Insider Tip: Meta needs you to be very fast & accurate with writing SQL. Being rusty with SQL because you use R or Python day-to-day is NOT a valid excuse at Meta. They have thousands of people who apply for this role, and the SQL screen is an easy black-and-white filter to remove candidates, so you should aim for flawless execution here.

The best way to practice for the technical screen is to solve real SQL interview questions asked by Meta. We covered these in our article 9 Meta/Facebook SQL Interview Questions and built an interactive coding-pad to help you practice:

Final Round: 4-5 Interviews On-Site

Anywhere from 1 to 3 weeks following the Technical Screen, you will hear if you’ve moved to the next round. The On-Site Meta Data Science Interview is split into 4 interviews of 45 minutes each focusing on a different topic:

• 💼 Format: Virtual video call
• ⏰ Duration: 45 minutes each
• 👤 Interviewer: Hiring Manager/Senior Data Scientist
• ❓ Questions: Product Case Study Questions, Metrics Definition Questions, Statistics & A/B Testing Questions, SQL Questions, Behavioral Questions

We'll cover each of these types of questions in the next section.

Meta Data Science Interview Questions

The Meta Data Science Interview questions can be broken into 7 major types of problems:

• 🎯 Product Metrics
• 📊 Analytical Execution
• 💡 Analytical Reasoning
• 🔍 Research Design
• 💻 Technical Analysis
• 📚Case Studies
• 🧠 Behavioral Questions

Let's cover each type of interview question, how to study for it, and then work through 35 leaked Meta Data Science interview questions.

Meta Product Metrics Questions

Meta product-metric questions focus on setting product goals, selecting the corresponding success metrics, measuring the impact of product changes at Meta, and diagnosing metric drops.

Some recently asked Product Metrics questions at Meta:

1. Meta's mobile app is experiencing high bounce rates and low session durations. How would you identify usability issues and define success metrics to optimize the user experience and increase engagement?
2. A user advocacy group raises concerns about the accessibility of Meta's platform for individuals with disabilities. How would you assess accessibility metrics and define success criteria to ensure inclusivity in product design and development?
3. Imagine you launched a feature to grow engagement of Facebook Groups. The Daily-Active-Users of groups goes up by 2%, but the average time-spent on Facebook Groups goes down by 3%. How would you determine if you should ship this feature?
4. Meta is trying to launch social shopping, similar to TikTok Shop. Without building a beta-test of the feature, how would you opportunity size the revenue impact from the feature?
5. Imagine Meta is planning to launch a new video feature aimed at young adults. How would you assess the product-market fit and define success metrics to ensure resonance with the target demographic?

To prepare, read our in-depth Product-Sense Interview Guide to get tips on:

• Defining a Product Metric
• Diagnosing a Metric Change
• Brainstorming Product Features
• Designing A/B Tests

Meta Analytics Execution Questions

Analytical Execution questions aim to assess your proficiency in statistics, data analysis, data and strategic decision-making based on data insights. Be prepared to provide specific examples from your past experiences and highlight the impact of your work on business outcomes.

6. Can you walk us through your approach to conducting exploratory data analysis (EDA) on a large dataset? How do you identify relevant insights and trends?
7. Describe a time when you had to develop and implement a machine-learning model to solve a business problem. How did you select the appropriate algorithm, and how did you validate the model's performance?
8. Discuss a project where you used data visualization techniques to communicate complex findings to stakeholders. How did you choose the most effective visualization methods, and what impact did your visualizations have on decision-making?
9. Tell us about a time when you had to extract and analyze data from multiple sources or databases. How did you ensure data integrity and consistency, and how did you handle challenges related to data quality or missing values?
10. Can you share an example of a data-driven recommendation or strategy you proposed to optimize product performance or user engagement? How did you measure the success of your recommendation, and what were the outcomes?

Meta Analytical Reasoning Questions

These questions assess your ability to think analytically, apply data science concepts to real-world problems, and communicate your reasoning effectively. Be prepared to explain your approach, justify your decisions, and provide clear and logical solutions to the challenges presented.

11. Meta's data science team is analyzing user engagement metrics for a new feature rollout on its social media platform. However, the data shows a significant drop in engagement rates shortly after the feature launch. How would you investigate the cause of the drop in user engagement, prioritize potential factors contributing to the decline, and propose data-driven strategies to address the issue?
12. Meta's advertising team is exploring ways to optimize ad targeting to increase revenue and improve ad relevance for users. However, ad click-through rates are lower than expected, indicating potential issues with targeting accuracy. How would you analyze user demographic and behavioral data to assess the effectiveness of ad targeting algorithms, and what strategies would you propose to improve targeting accuracy and ad performance?
13. Meta's product team is considering introducing a new feature that allows users to customize their profile settings. However, there are concerns about potential privacy implications and data security risks associated with the feature. How would you conduct a privacy impact assessment to evaluate the potential risks and benefits of implementing the new feature, and what analytical methods would you use to assess user privacy preferences and mitigate privacy concerns?
14. Meta's data science team is exploring ways to improve search relevance for users navigating its marketplace platform. However, search queries are returning irrelevant or inaccurate results, leading to frustration among users. How would you analyze user search queries and click-through behavior to identify issues with search relevance, and what machine learning techniques would you use to enhance search ranking algorithms and improve result accuracy?
15. Meta's data science team is investigating the impact of algorithmic bias on content recommendations in its news feed. Users have reported instances of bias in recommended content, leading to concerns about fairness and diversity. How would you quantify and measure algorithmic bias in content recommendations, and what analytical techniques would you use to identify biased patterns and mitigate the impact of bias on user experience and content diversity?

Meta A/B Testing & Research Design Interview Questions

Meta's goal is to understand the depth of your A/B testing skills with these questions. Get familiar with key terms like A/B testing, KPIs, longitudinal user behavior, and RCT.

16. Explain how you would set up a randomized controlled trial (RCT) to evaluate the effectiveness of a new privacy feature on Meta's messaging platform.
17. Describe a methodological approach you would use to assess the usability of a new user interface design for Meta's virtual reality applications.
18. What data sources would you leverage to evaluate the effectiveness of Meta's educational initiatives?
19. How would you recruit participants for interviews or focus groups, and what strategies would you use to ensure diverse perspectives are represented?
20. Can you explain how you would analyze the data to identify patterns and insights regarding ad targeting effectiveness?

For more practice, read our blog on 50 A/B Testing Interview Questions which also covers resources to learn this material in-case you haven't done much with product experimentation before.

Meta Technical SQL Questions

These questions assess your understanding of SQL, as well as your speed in writing SQL queries. The exact flavor of SQL you use doesn't matter – it's not a test of nitty-gritty syntax. Here's a few example SQL questions from Meta:

21. Page With No Likes (Meta SQL Interview Question)

Assume you're given two tables containing data about Facebook Pages and their respective likes (as in "Like a Facebook Page").

Write a query to return the IDs of the Facebook pages that have zero likes. The output should be sorted in ascending order based on the page IDs.

Table:

Example Input:

Table:

Example Input:

Example Output:

The dataset you are querying against may have different input & output - this is just an example!

p.s. If you have literally no idea how to solve this, maybe give our free SQL tutorial a try first?

22. Product vs. Square (Meta Statistics Interview Questions)

There are two games involving dice that you can play. In the first game, you roll two dice at once and receive a dollar amount equivalent to the product of the rolls.

In the second game, you roll one die and get the dollar amount equivalent to the square of that value. Which has the higher expected value and why?

This is the same question as problem #27 in the Statistics Chapter of Ace the Data Science Interview!

23. Weekly Churn Rates (Meta SQL Interview Question)

Facebook is analyzing its user signup data for June 2022. Write a query to generate the churn rate by week in June 2022. Output the week number (1, 2, 3, 4, ...) and the corresponding churn rate rounded to 2 decimal places.

For example, week number 1 represents the dates from 30 May to 5 Jun, and week 2 is from 6 Jun to 12 Jun.

Assumptions:

• If the last_login date is within 28 days of the signup_date, the user can be considered churned.
• If the last_login is more than 28 days after the signup date, the user didn't churn.

Table:

Example Input:

Example Output:

User ids 1001, 1002, and 1004 signed up in the first week of June 2022. Out of the 3 users, 1002 and 1004's last login is within 28 days from the signup date, hence they are churned users.

To calculate the churn rate, we take churned users divided by total users signup in the week. Hence 2 users / 3 users = 66.67%.

The dataset you are querying against may have different input & output - this is just an example!

Want some more SQL Questions? Try these 9 Meta SQL Interview Questions.

24. Case Study: User Engagement Retention Strategies at Meta

Question: You're tasked with analyzing user engagement on Meta's new social media platform. The product team wants to understand factors that influence user retention and identify strategies to improve engagement. Given access to user interaction data, how would you approach this analysis, and what insights would you provide to the product team?

Sample Answer:

1. Define key metrics related to user engagement and retention: Daily Active Users (DAU), Session Duration, and Churn Rate
2. Segment Users: Demographic Information, Usage Patterns, Engagement levels
3. Conduct an Exploratory Data Analysis (EDA) to understand user behavior
4. Use statistical methods to analyze correlations and other variables
5. Employ Machine Learning techniques: predictive modeling, identifying user segments with high churn risk, retention strategies
6. Communicate your findings through clear actionable insight

25. Case Study: Feature Impact Evolution at Meta

Question: Meta is launching a new feature aimed at enhancing user experience on its social media platform. As a Data Scientist, you're tasked with evaluating the impact of this feature launch on user engagement and satisfaction. How would you design and execute an experiment to assess the effectiveness of the new feature, and what metrics would you use to measure its success?

Sample Answer:

1. Form a clear hypothesis about the expected impact of the new feature on user engagement and satisfaction
2. Example: Users who experience the new feature will exhibit higher engagement metrics, such as increased time spent on the platform and higher interaction rates
3. Conduct A/B testing for treatment and control groups to test satisfaction
4. Collect relevant metrics: session duration, number of interactions, user feedback ratings, retention rates
5. Conduct a statistical analysis to compare the performance of the two groups, and calculate Key Performance Indicators (KPIs)
6. Interpret the results and draw actionable insights

Meta Behavioral Questions

These questions aim to assess your communication abilities and your decision-making skills.

26. Tell me about a time when you had to work on a challenging data science project. How did you approach the problem, and what was the outcome?
27. Describe a situation where you had to communicate complex technical concepts to a non-technical audience. How did you ensure effective communication, and what was the result?
28. Can you share an example of a time when you faced a setback or failure in a data science project? How did you handle it, and what did you learn from the experience?
29. Discuss a project where you had to collaborate with cross-functional teams or stakeholders. How did you manage differing priorities and opinions, and what was the outcome of the project?
30. Tell me about a time when you had to make a decision based on incomplete or ambiguous data. How did you approach the situation, and what were the implications of your decision?

For more insight into crafting kick-ass answers to behavioral questions, check out our Data Science Behavioral Interview Question Guide.

Preparation Tips for the Meta Data Scientist Interview

Now that you’ve learned everything there is to know about the interview process it’s time to prepare. You must navigate the interview process with confidence and precision, so take the time to prepare and refresh both your hard and soft skills.

Tips for the Day of Your Meta Interview:

1. Think out loud 🤔: Provide a narrative as you go through the problem so that the interviewer has insight into your thought process.
2. Deconstruct your problems 🛠️: Deconstruct complicated or ambiguous problems into groups, and combine the groups for a solution.
3. Hints 💡: Pivot your answer if your interviewer prompts you that you’re heading in the wrong direction.
4. Clarification 🔍: Ask clarifying questions during the interview.
5. Say why you’re interested in a career at Meta 🌟: Meta interviewers like to see people who know about our environment, projects, challenges, etc.
6. Questions ❓: Ask questions about Meta and analytics if there’s time.

7 Best Resources to Prepare for the Meta Data Science Interview

If you're serious about acing the Meta Data Science interview, one blog article ain't gonna cut it. Here's the 7 best resources to study next:

1. Khan Academy Statistics and Probability Course: good for the Meta analytical execution questions that cover prob/stats
2. DataLemur: 200+ SQL interview questions from Meta, and other big-tech companies like Amazon, Google, TikTok, Netflix etc.
3. Cracking the PM Interview by Gayle Laakman McDowell: good for the Meta Product-Sense & Product Case Study questions
4. Ace the Data Science Interview: written by 2 Ex-Facebook employees, this is the go-to resource for Acing the Meta Data Science Interview. The book has 201 real FAANG interview questions, including 11 from Facebook/Meta.
5. A/B testing Questions Blog: this guide walks you through how to run consumer experiments, which is a frequent topic due to how important product experimentation & interpreting test results is for Meta Product Analytics roles
6. Meta Careers Website: Visit this site to learn more about the company culture, values, and available data science roles
7. Meta ML/Engineering Blog: Get more familiar with the technical problems Meta's tackling.