Meta Data Scientist Interview guide

The recruiter screening will be a conversation with a recruiter, detailing your DS background, your past relevant projects, and a quick assessment of your skill sets based on your resume.

They will ask you about your areas of expertise and experience with relevant programming languages, frameworks, and tools. Do your skills align? Do you understand the role? Do you understand Meta's strategy and values?

The recruiter will also ask you to provide examples of projects you have worked on, and how you have addressed technical challenges in those projects. What matters here is clarity about real wins and the learning that came with them. Show concrete impact: how a contribution changed a project’s direction, tightened execution, or raised the bar for the team. Interviewers focus on these specifics because they show how an individual actually adds value in a collaborative environment.

Pro tip: Make sure to mention something memorable or reference something specific that might set you apart.

• Why do you want to join Meta?
• Why do you think you will be a good fit for the role?
• What responsibilities do you expect to have from your job at Meta?
• What makes you the best candidate for this position?

During this round, you will be asked a series of behavioral questions, followed by a case study that includes a SQL-based challenge. The case study primarily involves product-based questions that require candidates to provide KPIs and data to support their answers. This process is designed to assess a candidate's problem-solving skills, data analysis capabilities, and their ability to communicate their insights effectively.

The interviewer will also pay close attention to your previous experience in the field. This will likely involve questions about the types of projects you have worked on in the past, the types of data sets you have analyzed, and the techniques you have used to analyze them.

Note that for DSes in different teams at Meta, the initial screening and onsite emphasis can differ based on role specialization and team need. However, all candidates must demonstrate SQL proficiency. Meta's analytics stack relies on:

• Presto for ad-hoc queries (interactive, <1 sec to ~minutes)
• Spark for heavy ETL workloads (batch, can take hours)

Therefore, candidates should be prepared to write SQL primarily, and Python secondarily. During your recruiter screen, mention your SQL expertise and language preference (e.g., "I'm most comfortable in SQL; happy to code Python if needed"). Make sure you are able to talk explain your tradeoffs if you are asked to do a more advanced design..

Some teams, such as Marketplace, Ads, and Creator Economy, place special emphasis on causal inference and two-sided market experiments. These involve advanced concepts like:

• Network contagion effects (if we boost one actor, do others suffer?)
• Incremental attribution vs. total attribution
• Synthetic controls, difference-in-differences, instrumental variables

Example Question:

"You're running an experiment to boost Marketplace seller reach (by ranking them higher). How do you design the test to measure true incremental GMV (not just within-seller impact, but accounting for seller cannibalization)?"

If interviewing for these teams, we recommended that you prepare by studying “Trustworthy Online Controlled Experiments”, especially chapters 4–5 (which cover spillover and interference effects) and chapters 9–11 (which cover more advanced experimental designs).

• How would you measure Reels success?
• Diagnose a 20% engagement drop.
• Imagine you're analyzing Reels watch time. Write a SQL query to find the top 5 Reels creators (by total watch time) in the last 7 days, excluding creators who started less than 30 days ago. Also rank them within each country.
• Given a database schema and data, write a SQL query to extract specific information required to answer a given product-based question.
• How would you go about analyzing user engagement with a new feature and identifying areas for improvement?
• If you were tasked with improving the performance of a certain product, what KPIs would you track and how would you go about improving them?
• Can you describe a particularly challenging data analysis project you worked on in the past? What were some of the key challenges you faced and how did you overcome them?
• What types of data sets have you worked with in the past? Can you give examples of the size and complexity of these data sets?
• What data analysis techniques or tools are you particularly skilled in? Can you give examples of how you have used them in previous projects?

The final stage of the interview process will be an on-site interview comprising multiple different interview rounds. The primary rounds are explained below:

1. Technical Skills Round (45 min, Onsite): In this interview, you'll receive an open-ended product data problem (e.g., "Diagnose a 20% drop in Reels watch time over the past week. Walk me through your approach"). You will need to design a solution using SQL or Python, demonstrating how you structure the problem (first 5 mins), execute queries (next 20 to 30 minutes), and interpret and communicate tradeoffs (final 10 minutes). Don’t forget - your interviewer might provide new information or constraints as you go along, so you’ll need to keep adapting. Make sure to structure out loud. Interviewers want to see your thinking, not just the final answer. You might go: "Here's what I found: engagement is down, but watch time per viewer is flat. This suggests fewer users are visiting Reels (supply problem, not quality problem). My recommendation is to investigate creator incentives..."

Candidates should know how to write SQL queries that will run efficiently on Presto at scale. Meta’s actual engineering practices (the "VeloxCon 2025 Presto Performance" talk and several published Meta Engineering Blogs) confirm this technology stack is core. The Presto/Spark thinking is how you demonstrate practical awareness of Meta’s big-data environment during the Technical Skills round, signaling you are ready to work with Meta-scale data efficiently and thoughtfully. For insights into how similar roles are tested at other companies, you might find the Facebook Data Analyst Interview guide helpful. You can also book a Technical Screen Mock with a Meta coach on Prepfully.

1. Analytical Execution Round (45 min, Onsite): In this round, the questions will focus on understanding hypotheses for launching new features, quantifying tradeoffs of a feature in terms of metrics, and applying basic statistics concepts like mean/expected value, median, mode, percentiles, and common distributions. Advanced topics (power, MDE, Type I/II error) are also included. Expect questions on:

• Hypothesis Design & Statistical Power: "We're running an A/B test to increase Reels watch time with a new ranking algorithm. What's the null hypothesis? Alternative? Is this one-tailed or two-tailed? And how many samples do we need?"
• Type I vs. Type II Error Trade-Offs: "If we lower our significance threshold from p < 0.05 to p < 0.10, what changes? Should we do it?"
• Metric Trade-Offs & Guardrail Metrics: "This A/B test shows +5% engagement but -3% time-per-session. Is this a win for Meta?"
• Statistical Significance vs. Practical Significance: "Your test shows a +0.1% statistically significant lift in revenue with N=1M users. Is this launch-worthy?"
  

1. Analytical Reasoning Round (45 min, Onsite): This is the highest-bar technical round. Interviewers assess your ability to think causally, design experiments for complex situations, and tell data stories that influence decisions. It focuses on:

• Casual Inference & Incremental Testing (New 2024–2025 Focus): "Marketplace sellers who list items on weekends have 30% higher sales. Should we offer them a seller bonus to list on weekends?"
• Two-Sided Market Experiments & Interference: "You're testing a new creator bonus program (to increase Reels supply). How do you design the experiment to measure true incremental impact, not just intra-creator effects?"
• Observational vs. Experimental Design Trade-Offs: "Designing a full RCT for a new privacy feature takes 2 months. Our PM needs an answer in 2 weeks. What do you do?"
• Bias Identification & Mitigation: ""This A/B test ran during holiday shopping (Nov–Dec). How do you detect seasonal bias?"
• Storytelling & Guardrail Tensions: "Your test shows engagement +8% (win!), but creator satisfaction -2% (slight loss). Your PM asks: 'Should we launch?' How do you present this?"

1. Behavioral Round (45 min, Onsite): In this round, you will be asked hypothetical questions on things you might encounter at Meta, as well as behavioral questions that draw from your previous experience. Interviewers assess how you navigate Meta's culture: ambiguity, data-driven decision-making, influence without authority, and inclusion. Common questions in this round include:

• Tell me about a time when requirements changed mid-project. How did you adapt?
• Tell me about a time you disagreed with a teammate. How did you handle it?
• Describe a time you ensured all team members felt included in a project.
• Tell me about a project that didn't go as planned.

To do well in this round, prepare 4 to 5 different stories. You want to tell stories that highlight a range of skills Meta values, for instance handling ambiguity, managing cross-functional teams, having a learning mindset, etc. Use STAR method to frame your answers and prepare for follow-up questions. Interviewers often ask “What would you do differently?” or “How did this experience change your approach?” Prepare honest reflections. You can practice with a peer or mock interviewer (e.g., Prepfully coach).

• How would you approach a problem where you have a large dataset and need to find the most common values in a specific column?
• Write a function that takes in a list of integers and returns the sum of all the even numbers in the list.
• Given a string, write a function that determines whether it is a palindrome.
• How would you determine whether a new feature that you launched had a positive impact on user engagement?
• If you had to choose between two different metrics to measure the success of a feature, which metric would you choose and why?
• If you were given data on user engagement for a new feature, how would you determine whether the engagement levels were statistically significant?
• How would you design an experiment to determine whether users prefer a new feature over an existing one?
• What are some potential biases that could occur in a study measuring the effectiveness of a new feature, and how would you account for them?
• Given a data visualization, how would you interpret the findings and communicate them to a non-technical audience?
• How do you approach working with a team to solve a difficult problem?
• Describe a time when you had to adapt to a new situation or unexpected challenge at work.
• How do you ensure that all team members feel included and valued in a project?