As a Data Scientist at Meta, you occupy a strategic role that sits at the intersection of quantitative analysis, product strategy, and engineering. You are not just a resource for pulling numbers; you are a core driver of the product roadmap for platforms that serve billions of users, including Facebook, Instagram, WhatsApp, and Reality Labs. Your work directly influences how people connect, build communities, and grow businesses around the world.

In this position, you are expected to apply technical rigor to one of the richest datasets in existence. You will partner closely with Product Managers, Engineers, and Researchers to identify opportunities, measure progress, and solve complex problems. Whether you are optimizing the feed algorithm for Instagram, improving monetization strategies for Ads, or defining engagement metrics for new VR experiences, your insights will shape high-stakes decisions.

The role demands a unique balance of skills: you must possess the technical ability to query and model massive datasets, the statistical knowledge to design rigorous experiments, and the product intuition to ask the right questions. At Meta, a Data Scientist is a storyteller who uses data to advocate for the user experience and drive tangible business impact.

The following questions are representative of what candidates face at Meta. They are designed to test your ability to think on your feet and apply your skills to real-world scenarios. Do not memorize answers; instead, practice the structure of your response.

Product Sense & Metrics

This category tests your ability to measure success and diagnose health.

• "How would you measure the success of the Facebook Marketplace?"
• "Instagram Stories usage is down 10%. How would you investigate?"

Preparing for a Meta interview requires a shift in mindset. You are not just being tested on your ability to write code or solve equations; you are being evaluated on your ability to drive impact in a fast-paced, ambiguous environment. The interviewers are looking for future colleagues who can take a vague problem and turn it into an actionable data strategy.

You will be evaluated on the following key criteria:

Product Intuition (Analytical Reasoning) This is the ability to translate abstract business problems into concrete data questions. Interviewers assess whether you can define the right success metrics, identify trade-offs (e.g., engagement vs. latency), and understand the ecosystem effects of a product change. You must demonstrate that you understand why a product exists and how users interact with it.

Technical Execution (SQL & Statistics) Meta places a high premium on your ability to retrieve and manipulate data accurately. You are expected to write clean, efficient, and bug-free SQL. Furthermore, you must demonstrate a solid grasp of probability and statistics, particularly regarding hypothesis testing and experimentation, to prove that your insights are statistically valid.

Communication and Influence You must be able to "tell a data-driven story." It is not enough to find the answer; you must explain your methodology and recommendations clearly to stakeholders who may not be technical. Interviewers look for structured thinking and the ability to drive consensus among cross-functional partners.

Culture and Collaboration Meta values candidates who move fast and focus on impact. You will be assessed on your ability to work autonomously, navigate ambiguity, and collaborate with diverse teams. Your behavioral answers should highlight how you handle conflict, manage failure, and lead initiatives.

The interview process for Data Scientists at Meta is highly structured and standardized. It typically begins with a recruiter screening to discuss your background and interest in the role. If you pass this stage, you will move to a technical screening (usually video-based) which focuses heavily on SQL proficiency and product sense. This screen is a filter to ensure you have the core technical skills required for the job.

The final stage is the "Virtual Onsite" or "Loop," which consists of four to five separate interviews. These rounds are split into specific competencies: Analytical Reasoning (Product Sense), Analytical Execution (Applied Stats/Math), Technical Skills (SQL/Coding), and Behavioral/Leadership. For Research Scientist roles, this loop may also include a presentation of your past research. The process is rigorous, and interviewers are known to stick to a script to ensure fairness and consistency across candidates.

Expect the process to move relatively quickly once you are in the loop, though scheduling can sometimes take time depending on interviewer availability. Meta’s process is designed to be transparent; recruiters often provide detailed preparation materials. However, the interviews themselves are intense, often requiring you to solve problems in real-time with limited guidance.

This timeline illustrates the typical flow from application to offer. Use this to plan your preparation: ensure your SQL and product frameworks are solid before the Technical Screen, and reserve your deep dives into behavioral stories and advanced statistics for the period leading up to the Onsite Loop.

The Meta Data Scientist interview is compartmentalized into specific types of rounds. Understanding the distinct goal of each round is critical for success.

Analytical Reasoning (Product Sense)

This is arguably the most critical and open-ended part of the interview. You will be presented with a vague product scenario (e.g., "How would you measure the success of Instagram Stories?" or "We want to launch a new feature for Groups; should we?").

Be ready to go over:

• Metric Definition: Identifying North Star metrics, secondary metrics, and guardrail metrics (to prevent negative side effects).
• Ecosystem Measurement: Understanding how a change in one surface (e.g., Feed) impacts another (e.g., Reels).
• Experimentation Design: Setting up A/B tests, selecting randomization units, and determining sample sizes.
• Trade-off Analysis: Making decisions when primary metrics go up but guardrail metrics (like latency or revenue) go down.

Example questions or scenarios:

• "How would you measure the health of the Facebook friend request system?"
• "We noticed a drop in Ad revenue on Tuesdays. How would you investigate?"
• "Design an experiment to test a new recommendation algorithm for Marketplace."

Technical Skills (SQL & Data Processing)

This round tests your raw ability to pull data. Unlike some companies that allow pseudo-code, Meta interviewers often expect syntactically correct, executable SQL. You may use a platform like CoderPad.

Be ready to go over:

• Complex Joins: Mastering LEFT JOIN, INNER JOIN, and SELF JOIN scenarios.
• Aggregation and Filtering: Using GROUP BY, HAVING, and window functions (RANK, LEAD, LAG) effectively.
• Data Cleaning: Handling NULL values, duplicates, and formatting timestamps.
• Efficiency: Writing queries that are optimized for performance, not just correctness.

Example questions or scenarios:

• "Given a table of user actions, calculate the day-over-day retention rate."
• "Find the top 3 users with the highest engagement per country."
• "Calculate the percentage of users who sent a message within 24 hours of adding a friend."

Analytical Execution (Statistics & Probability)

This round bridges the gap between raw data and business insights. It tests your statistical knowledge and your ability to apply it to business problems.

Be ready to go over:

• Probability Theory: Bayes' Theorem, conditional probability, and expected value.
• Statistical Inference: Hypothesis testing, p-values, confidence intervals, and bias/variance trade-offs.
• Modeling Intuition: When to use regression vs. classification, and how to interpret model coefficients.
• Advanced concepts: Power analysis and calculating Minimum Detectable Effect (MDE).

Example questions or scenarios:

• "We ran an A/B test and the p-value is 0.06. What is your recommendation?"
• "How would you estimate the probability that a user clicks on an ad given their history?"
• "Explain the difference between correlation and causation to a non-technical Product Manager."

Behavioral & Leadership

Meta assesses "culture add" rather than just culture fit. They want to see how you navigate difficulties and drive impact.

Be ready to go over:

• Conflict Resolution: Disagreeing with a PM or Engineer and resolving it with data.
• Driving Impact: Taking a project from an ambiguous idea to a launched feature.
• Learning from Failure: A time your analysis was wrong or your project failed, and what you learned.

As a Data Scientist at Meta, your day-to-day work revolves around turning massive amounts of data into product decisions. You are responsible for the entire lifecycle of data analysis. This starts with exploratory analysis to understand user behaviors and identify new opportunities for growth or efficiency. You will often write complex pipelines to aggregate data from different logs to build a clear picture of the product ecosystem.

A significant portion of your time will be spent on experimentation. You will design rigorous A/B tests, determine sample sizes, and analyze the results to recommend whether to launch, iterate, or kill a feature. You are the gatekeeper of statistical validity, ensuring that the team does not make decisions based on noise.

Collaboration is central to the role. You will work side-by-side with Product Managers to define roadmaps and with Engineers to ensure data logging is accurate. You will also present your findings to leadership, requiring you to synthesize complex technical details into clear, actionable business recommendations. In senior roles, you are also expected to mentor junior scientists and influence the broader data culture of the organization.