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 biased! I worked on Facebook’s Growth Team and wrote a best-selling book with my bestie whose an Ex-Facebook Data Scientist. We've got a lot of love for Meta, 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 to Ace the Meta interview, just like we did back in the day:

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

• Stages of the Interview Process
• 6 Analytical Execution Questions
• Preparing for the Analytical Reasoning (Product-Sense) Round
• 12 Analytical Reasoning Questions
• A/B Testing 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?". This might seem like an obvious and easy question, but SO MANY folks we talk to fail this because they yap on-and-on about fine-tuning LLMs and deep learning and Computer Vision. But this answer sucks because Product Analytics Data Scientists at Meta DO NOT build Machine Learning models.

Instead, try to use the keywords from the job description, because a Meta recruiter is trained to listen for these phrases:

A good answer to "Why Product DS @ Meta?" would incorporate:

• your passion for working cross-functionally with PMs and business stakeholders
• a story about how you defined key product metrics to better understand and track the performance of a product or business line
• a time you ran an A/B test, and the impact that experiment had on the future product roadmap
• why you want to keep doing this past product data science work, but now on a product that serves 2-billion people!

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, where the interviewer can watch you code live, similar to the DataLemur interface. The dialect of SQL you use doesn't matter much, so if you use a mainstream one like MySQL or PostgreSQL or SQL Server you should be good to go! Here's what the

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.

The best way to practice for the SQL 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 on DataLemur to help you practice:

Intro Product Sense Question As Part of Phone Screen

You'll also be asked a light "Product Sense" question as part of your technical phone screen. This question is usually related to the SQL coding question. For example, if your SQL coding question is about analyzing churn of Facebook Marketplace users, you might first be asked an open-ended metrics question like "What are some metrics you'd track to measure the health of Facebook Marketplace?"

Final Round: 4-5 Interviews On-Site

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

• 💼 Format: Virtual video call
• ⏰ Duration: 45 minutes each
• 👤 Interviewer: Hiring Manager/Senior Data Scientist
• ❓ Topics: Analytical Execution, Analytical Reasoning, DS Technical Skills, & Behavioral Questions

In case you have no idea what an "analytical execution" or "analytical reasoning" round entails – you're not alone – we think Meta's terminology is weird too. Let's dive into each interview round, and some leaked questions, in the next section.

30 Meta Data Science Interview Questions

The Meta Data Science onsite interview covers:

• Analytical Execution: probability, statistics, hypothesis testing
• Analytical Reasoning: product metrics definition, evaluating tradeoffs, A/B testing
• DS Technical Skills: SQL
• Behavioral Interview Questions

Let's examine each round in more detail, and cover 30+ leaked Meta DS interview questions.

Meta Analytical Execution Round

The Analytical Execution round tests your probability skills, statistical foundations, and raw math brainpower (which Meta calls mental agility). Specific topics include:

• Elements of descriptive statistics (mean, median, mode, percentiles)
• Common probability distributions (binomial, normal, poisson)
• Combinations, Permutations, Conditional Probability, and Bayes' Theorem
• Issues analyzing real-world data (outliers, missing values, etc.)
• Key statistics concepts (Law of Large Numbers, Central Limit Theorem, etc.)
• Conditional probabilities, including

This round is quite tricky, because most Data Scientists day-to-day don't use Bayes' Theorem or do calculations involving binomial random variables. It can be exceptionally difficult for seasoned Data Scientists, who might have last touched these concepts in an undergrad stats class, 10+ years ago.

Meta doesn't give AF.

The only way to crush this round is to review your prob + stats foundations, and of course practice (starting with these next 6 questions).

6 Meta Analytical Execution Interview Questions

1. On Instagram, the probability of a user watching a story to completion is 0.8. If a user posts a sequence of 4 stories, what is the probability that a viewer will watch all 4 stories? What about at least 2 stories?
2. What is the difference between Type I and Type II errors in hypothesis testing?
3. Say you roll a die three times. What is the probability of getting two sixes in a row?
4. Can you explain what a p-value and confidence interval are, but in layman's terms?
5. Explain the concept of covariance and correlation. How are they different, and what do they measure?
6. A Facebook Ads analyst is investigating the effectiveness of a new ad targeting algorithm. As a general baseline, they know that 1% of all users who see an ad convert (make a purchase). The new algorithm correctly identifies 80% of users who will convert for an ad. The algorithm also incorrectly flags 10% of non-converting users as likely to convert. Given that the algorithm has flagged a user as likely to convert, what is the probability that this user will actually convert?

To practice more of these analytical execution interview questions, read Chapters 5 and 6 in Ace the Data Science Interview:

Analytical Reasoning / Product-Sense Round

The Analytical Reasoning round tests your general product-sense/business-sense. Unlike the execution round, there's usually no math – and no one right answer. Instead, Analytical Reasoning rounds usually have a long back-and-forth discussion around some specific new product or feature.

Meta might ask you:

• what data would you analyze to see if building this new product/feature is worth it?
• how would you design an A/B test for the new feature?
• what A/B testing pitfalls might you encounter?
• what success metrics would you track, to see if this new feature is good?
• what guardrail or counter-metrics would you track?
• if some key metric went up, but a different metric got worse, how would you determine whether to ship the feature?
• if there suddenly was a drop in some key metric, how would you troubleshoot the root-cause of the metric change?

To prepare for Meta's analytical reasoning round, 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

You'll also want to read the Product-Sense chapter of Ace the Data Science Interview, which has 30 real product-sense questions with solutions, along with proven frameworks to tackle these open-ended product metrics & A/B testing questions.

Once you read the above resources, you're ready to tackle 12 real Meta analytical reasoning interview questions.

12 Analytical Reasoning Interview Questions

1. Meta's mobile app is suddenly experiencing high bounce rates and low session durations. How would you troubleshoot this issue?
2. A user advocacy group raises concerns about the accessibility of Meta's platform for individuals with hearing disabilities. What are some product improvements that could be made with Facebook Live and Facebook videos? What metrics would you define, to see if your features had a positive impact?
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?
6. Meta's data science team is analyzing user engagement metrics for a new close-friends Reels tab. 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?
7. 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?
8. 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?
9. 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?
10. 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?
11. The PM responsible for Facebook events has a new idea to drive engagement – when your friends mark that they'll attend an event, you will get a notification. How would you measure the success of this notification? What counter-metrics would you look at?
12. The Instagram Monetization team would love to double the amount of ads shown on Instagram – it's the quickest way to nearly double revenue over-night. What do you think about this idea? How would you determine the optimal ad-load for Instagram?

6 Meta A/B Testing & Interview Questions

Meta often goes deeper into A/B testing and Research Design questions. They might hammer the maths/stats of A/B testing in the Analytic Execution round, and cover higher-level product experimentation questions in the Analytical Reasoning rounds.

To prepare, here's 6 real A/B testing questions asked by Meta:

1. 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.
2. Describe a methodological approach you would use to assess the usability of a new user interface design for Meta's virtual reality applications.
3. We try a new ML algorithm which improves ad targeting for e-commerce companies, who run a special type of ad known as the "shoppable feed ad". We want to test if this new ML algorithm is better. How do we test it? How many ads, or ad viewers, or advertisers, do we need to collect data from before we can reach a statistically significant result?
4. How would you recruit participants for interviews or focus groups, and what strategies would you use to ensure diverse perspectives are represented?
5. If you have an experiment, but multiple hypothesis, what could go wrong? How do you control/correct for the potential pitfalls of multiple hypothesis testing?
6. What's the novelty effect in A/B testing? How can it be identified and accounted for?

For more product experimentation 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. Also read pages 246-250 of Ace the Data Science Interview for a crash-course on the most popular A/B testing concepts that occur in interviews.

Meta Technical SQL Questions

Meta's technical skills round during the onsite interview is basically all about SQL. Just like the technical phone screen round, the exact flavor of SQL you use doesn't matter – it's not a test of nitty-gritty syntax. It's a test of how accurately and quickly can you translate a business question into a SQL query that gets the answer.

To get faster at SQL, try to solve at least 50 out of the 200+ FAANG SQL questions on DataLemur. Aim to solve a DataLemur SQL medium difficulty question in ~5 minutes, and a DataLemur Hard in ~10 minutes.

Here's a few example SQL interview questions from Meta:

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?

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.

Meta Data Science Behavioral Questions

These behavioral questions aim to assess your communication abilities and your decision-making when it comes to common Data Science conflicts & challenges you'll run into a workplace like Meta.

1. 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?
2. 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?
3. 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?
4. 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?
5. 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?
6. Give a time when you had to influence and push a stakeholder to make a decision that they don’t necessarily agree with, but the data supports.

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

7 Best Resources 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:

1. Cracking the PM Interview by Gayle Laakman McDowell: good for the Meta Product-Sense questions (Analytical Reasoning Round)
2. Khan Academy Statistics and Probability Course: good for the Meta analytical execution questions that cover prob/stats
3. DataLemur: 200+ SQL interview questions from Meta, and other big-tech companies like Amazon, Google, TikTok, Netflix etc.
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 ML/Engineering Blog: Get more familiar with the technical problems Meta's tackling.
7. 1:1 Mock Meta Data Science Interview: Get 1:1 coaching with me (Nick Singh) – I'm ex-Meta and have helped 30+ land DS/DE/SWE offers at Meta!

You can also look at Data Science interview questions from other competitive companies, like these Microsoft Data Science interview questions or the TikTok Data Science Interview Guide.

6 Miscellaneous Tips to Ace 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. Don't freeze up!
2. Deconstruct SQL problems 🛠️: Deconstruct complicated or ambiguous SQL interview questions into smaller groups, solve the sub-problems, and combine them for a final solution.
3. Hints 💡: Pivot your answer if your interviewer prompts you that you’re heading in the wrong direction. Be open to the interviewer subtely guiding you if you go off track!
4. Clarification 🔍: Ask clarifying questions during the interview, especially in product-sense questions.
5. Say why you’re interested in a career at Meta 🌟: Meta interviewers like to see people who know about the company culture, products, and challenges. Don't admit to never using Facebook because it's for grandmas.
6. Questions ❓: Ask questions about Meta and their sub-team IF there is time. But don't stress too much, Meta interviews are mostly decided by technical performance. And obviously don't ask about pay (it's good), work-life balance (it's bad), and how they like it (they have to lie).

Also check out these use cases of Meta using data science in their day-to day operations.

Want to learn how other companies use data science? Check out these blogs!

• Amazon Data Science
• Walmart Data Science
• JP Morgan Data Science