Preparing for a Meta Data Science interview requires a shift in mindset. You are not just being tested on your ability to write code; you are being evaluated on your ability to solve business problems using data.

Key Evaluation Criteria:

• Product Intuition (Product Sense) – This is often the most critical and challenging component. Interviewers assess your ability to translate vague business questions into concrete metrics. You must demonstrate how you would measure success, diagnose metric movements (e.g., "Why did Likes drop by 10%?"), and make launch decisions.
• Technical Execution (SQL & Coding) – You must demonstrate fluency in data manipulation. Meta expects you to write clean, optimized SQL (or Python/Pandas) to extract and transform data. The focus is on correctness and efficiency in solving realistic data retrieval problems.
• Analytical & Statistical Rigor – You need a strong grasp of applied statistics. This includes hypothesis testing, designing A/B experiments, understanding statistical significance, and selecting the right machine learning models for specific problems (e.g., classification vs. regression).
• Communication & Influence – You will be tested on your ability to explain complex technical concepts to non-technical stakeholders. The "Behavioral" rounds focus on how you handle conflict, drive impact, and align with Meta’s core values.

The interview process at Meta is standardized yet rigorous, designed to minimize bias and maximize signal on your core competencies. Based on recent candidate experiences, the process is generally efficient but demands significant preparation.

Typically, the process begins with a recruiter screen to assess your background and interest. This is followed by a Technical Screen (usually video), which focuses on a mix of SQL coding and basic product/analytical questions. If you pass this stage, you move to the Virtual Onsite (the "Loop").

The Onsite usually consists of 4–5 separate interviews. For the standard Data Science track, expect a heavy emphasis on Product Analytics (Case Studies) and SQL. However, depending on the specific team (e.g., Core ML, Ads, or XR Insight), the process may skew heavily toward Machine Learning design and algorithmic coding. You should clarify with your recruiter whether your loop is "Product-focused" or "Systems/ML-focused," as recent reports indicate variations where candidates faced deep coding challenges and ML system design questions.

The timeline above illustrates the typical progression from initial contact to the final decision. Use this to pace your study schedule: focus on SQL and basic stats for the screen, then broaden your preparation to include deep product cases and behavioral stories for the onsite phase. Note that the gap between the screen and the onsite is the best time to practice "mock" interviews for the Product Sense round.

Meta’s interview loops are structured to test specific competencies in isolation. You should prepare for distinct "modules" of questioning.

Product Interpretation & Analytics (The "Case" Study)

This is the defining interview for Meta Data Scientists. You will be given an open-ended scenario regarding a Meta product.

• Why it matters: It simulates your daily work—taking an ambiguous problem and structuring a data-driven solution.
• Strong performance looks like: A structured approach (Frameworks like CIRCLES are helpful). You should clarify the goal, define the user, propose specific metrics (North Star vs. Counter-metrics), and design an experiment.

Be ready to go over:

• Metric Definition – How to measure "engagement," "success," or "quality."
• Debugging Metrics – Investigating sudden changes in data (e.g., "Time spent on Instagram Reels dropped 5% yesterday. How do you investigate?").
• Experimentation – Designing A/B tests, choosing randomization units, and determining sample sizes.

Technical Skills (SQL & Algorithms)

You will have a dedicated coding round. For general DS roles, this is primarily SQL. For ML-focused roles, this includes Python/Algorithms.

• Why it matters: You must be able to pull your own data.
• Strong performance looks like: Writing syntax-perfect SQL on a whiteboard or shared doc without an IDE. Handling edge cases (NULLs, duplicates) and using advanced joins/window functions.

Be ready to go over:

• SQL Joins & Aggregations – Self-joins, LEFT JOIN vs INNER JOIN, GROUP BY.
• Window Functions – RANK(), LEAD(), LAG(), and running totals.
• Algorithmic Coding (ML Track) – LeetCode Easy/Medium questions involving arrays, strings, or hashmaps.

Applied Statistics & Machine Learning

Depending on the role, this can range from basic probability to full ML system design.

• Why it matters: You need to validate your insights and build predictive models.
• Strong performance looks like: clearly explaining why you chose a specific model or test, not just the math behind it.

Be ready to go over:

• Probability – Bayes' Theorem, expected value.
• Statistical Inference – P-values, confidence intervals, bias vs. variance.
• Machine Learning – Logistic regression, Random Forests, overfitting/underfitting, and Recommendation Systems (specifically for News Feed/Ads roles).

As a Data Scientist at Meta, your day-to-day work is a mix of deep technical analysis and high-level strategy. You are responsible for the entire data lifecycle: from instrumentation (logging) to analysis and final recommendation.

You will spend a significant portion of your time defining and monitoring metrics. You will collaborate with engineering to ensure the right data is being captured. Once data is available, you will build pipelines (using SQL/Presto/Spark) to aggregate this data into dashboards that track product health.

Beyond reporting, you are the voice of data in product reviews. You will design and analyze A/B tests to validate new features. If a test result is ambiguous (e.g., clicks went up, but sessions went down), it is your job to dig into the data, segment by user type, and provide a launch/no-launch recommendation.

For roles in specialized teams like XR Insight or Ads, you may focus more on building and tuning machine learning models to personalize content or improve ad targeting efficiency. This involves feature engineering, model training, and offline/online evaluation.