Meta Data Scientist Interview Guide

Meta does not publish how long the Data Scientist process takes. Treat the stages below as preparation categories, not as confirmed Meta round names.

• Screen: Rehearse a concise account of your experience, analytical decisions, and interest in the role.
• Take-home: Practice turning an open dataset into a reproducible analysis with explicit assumptions, checks, and conclusions.
• Coding: Solve SQL and data-manipulation tasks while explaining correctness, edge cases, and tradeoffs.
• Case: Structure an ambiguous product question, choose metrics, propose analysis, and discuss possible confounders.
• Behavioral: Present specific examples of collaboration, disagreement, failure, learning, and influence.
• Onsite: Practice switching between technical analysis, product reasoning, and concise communication without changing your core evidence.

Data Scientist interviews generally examine several connected abilities. Your answers should make both the analysis and the reasoning visible.

• Statistics and experimentation: Define hypotheses, select metrics, reason about power and bias, interpret uncertainty, and distinguish correlation from causation.
• SQL and coding: Translate business questions into correct queries or code, test edge cases, and explain intermediate results.
• Modeling judgment: Choose methods that fit the decision, establish sensible baselines, identify leakage, and explain tradeoffs between accuracy and interpretability.
• Communication: State the decision first, support it with evidence, and explain limitations in language a non-expert can act on.

Meta describes its initial software engineering technical screen as a 45-minute conversation with a Meta engineer: 5 minutes of introductions, 35 minutes of coding, and 5 minutes for candidate questions. For a remote screen, the engineer sends a collaborative editing tool; an in-person candidate uses a whiteboard.

The full software engineering loop includes general coding questions, design, and a behavioral interview about prior work and motivation. The design and behavioral interviews each last 45 minutes, while the loop coding interview also follows a 45-minute format with 35 minutes devoted to coding.

Do not transfer that software engineering structure to the Data Scientist role as company policy. Data Scientist preparation should give equal attention to experimental reasoning, SQL, modeling choices, and explaining results, rather than treating general coding as the sole technical focus.

Build a preparation plan that can expand or compress once recruiting dates arrive. Keep separate practice sets for SQL, statistics, experimentation, cases, modeling, and behavioral evidence so weak areas are easy to revisit.

The challenge is breadth: a technically correct answer can still fall short if assumptions remain hidden or the conclusion does not support a decision. Under time pressure, clarify the question, state an approach, work through the analysis, check the result, and close with a recommendation and limitations.

Mirror the evaluation areas instead of studying each topic in isolation.

• Refresh foundations: Review probability, estimation, hypothesis testing, experimental design, causal pitfalls, and metric construction.
• Practice implementation: Write SQL and code from a blank editor, then test nulls, duplicates, unusual groups, and boundary conditions.
• Exercise judgment: Compare candidate methods, name a baseline, explain validation, and identify what would change your choice.
• Work through cases: Begin with the decision and population, then define success, propose analysis, anticipate bias, and recommend a next step.
• Prepare evidence: Build concise stories that show your individual contribution, reasoning, collaboration, and measurable effect without overstating causality.
• Rehearse aloud: Use a mock interview to practice concise explanations and follow-up questions across technical and non-technical topics.