Meta Data Scientist Interview Guide (2025 Update) Real Questions, Practical Advice, and Prep Plan from Successful Candidates --- Table of Contents 1. Introduction 2. Hiring and Application Process 3. Interview Structure & Rounds 4. Technical Interview — SQL & Product Case 5. Onsite Interviews Breakdown 6. Statistics & Analytical Reasoning 7. Analytical Execution & Case Studies 8. SQL Onsite Round 9. Behavioral & Leadership Questions 10. Preparation Timeline & Strategy 11. Recommended Resources 12. Final Tips --- 1. Introduction <a name="introduction"></a> Landing a Data Scientist (Analytics) role at Meta is one of the most competitive goals in the data industry. With billions of users and data-driven decision-making embedded in every product — from Instagram to Threads to Meta AI — these interviews test not only your technical ability but also your product sense and structured thinking. This updated guide combines real Meta interview experiences with verified questions and solutions from Prachub.com, helping you understand exactly what to expect and how to prepare efficiently. --- 2. Hiring and Application Process <a name="application"></a> Channels to Apply * Referrals (Highly Recommended): * Most successful Meta candidates get interviews through referrals. * Reach out to current employees who can advise you on team alignment and expectations. * Recruiter Outreach: * Meta recruiters often contact experienced data scientists on LinkedIn. * Be prepared with a tailored resume emphasizing impact metrics. * Direct Applications: * Submit via Meta Careers. * University recruiting is also an option for new graduates. * Highlight core technical stack: ` Python, SQL, R, Pandas, Scikit-learn,, BigQuery, Presto, Tableau, A/B Test, Product Analytics, Causal Inference ` --- 3. Interview Structure & Rounds <a name="rounds"></a> The Meta Data Scientist interview usually spans 4–6 weeks, with two main phases: Phase 1: Technical Screening (45–60 min) * SQL questions * Product case follow-up question * Optional statistics or probability component Phase 2: Onsite Interviews (4 Rounds) 1. Analytical Reasoning 2. Analytical Execution 3. SQL (advanced) 4. Behavioral / Leadership --- 4. Technical Interview — SQL & Product Case <a name="technical"></a> Meta’s technical interview heavily focuses on SQL and product analytics reasoning. The format often follows this pattern: 1. SQL question first — write a query using real product data context. 2. Product case follow-up — use your query results to discuss product metrics or experiment design. For example: * SQL questions about Group Calls such as Question 4902 and Question 4685 * Followed by a product case like Question 4551, which continues the same Group Call scenario. What to Focus On * SQL skills: Joins, CTEs, window functions, aggregations. * Product sense: Translating query outputs into actionable insights. * Metric thinking: Defining DAU/MAU, retention, engagement rate, CTR, etc. * Experimentation: Designing tests, measuring lift, and interpreting results. > 💡 Tip: Many SQL and product case questions at Meta are linked — always connect your data manipulation to a real product story. --- 5. Onsite Interviews Breakdown <a name="onsite"></a> The onsite rounds test depth, clarity, and reasoning. Here’s what each round covers: 1. Analytical Reasoning — statistics, probability, and foundational ML. 2. Analytical Execution — applied product analytics and experiment diagnosis. 3. SQL — advanced querying and metric definition. 4. Behavioral — leadership, collaboration, and communication. --- 6. Statistics & Analytical Reasoning <a name="statistics"></a> Core Topics to Master * Law of Large Numbers * Central Limit Theorem * Confidence Intervals & Hypothesis Testing * Two-sample t-test & z-test * Expected Value & Variance * Bayes’ Theorem * Distributions: Binomial, Normal, Poisson * Model Evaluation: Precision, Recall, F1, ROC-AUC * Feature Selection and Regularization (Lasso, Ridge) Example Question Real analytical reasoning question: 👉 Fake Account Detection Problem You’ll be asked to compute conditional probabilities using Bayes’ theorem, estimate expected value, and discuss model evaluation metrics. --- 7. Analytical Execution & Case Studies <a name="execution"></a> This is the most Meta-specific and most important round. It mirrors real business scenarios — diagnosing metric drops, designing A/B experiments, and evaluating trade-offs. Key Example: Instagram Reels Engagement Drop — Analytical Execution Question How to Prepare * A/B Experimentation: power, significance, MDE, p-values, guardrail metrics. * Funnel Analysis: conversion rate across multiple stages. * Cohort Analysis: retention and reactivation by user segments. * Metric Design: choose primary, secondary, and guardrail metrics. * Trade-offs: short-term engagement vs. long-term retention. * Product Familiarity: Understand Meta’s ecosystem — Threads, Instagram, Meta AI, WhatsApp, Oculus — and their co