FAANG Data Scientist Interview Questions: 2026 Guide
FAANG (Meta, Amazon, Apple, Netflix, Google/Alphabet) data scientist loops share a common shape: a recruiter screen, a SQL/statistics technical screen, a case or take-home analysis, and 3-5 onsite rounds covering experimentation, applied ML, and behavioral fit.
What to Expect
| Round | Format | Focus | Difficulty |
|---|---|---|---|
| Recruiter screen | 20-30 min | Background, role scope, team matching | Low |
| Technical screen | 45-60 min | SQL queries, probability/statistics fundamentals | High |
| Case/take-home | 60-120 min | Metric analysis, A/B test design, or applied ML problem | High |
| Onsite rounds | 3-5 rounds, 45 min each | Experimentation design, product/business sense, behavioral, sometimes coding | High |
Prep time: 4-6 weeks — the loop overlaps heavily across companies (SQL, stats, experimentation, one behavioral round per company's specific values), so prep is largely transferable, with company-specific behavioral framing as the main variable.
Frequently Asked Questions
Do all FAANG companies ask the same behavioral questions? No — each company frames behavioral rounds around its own values (Amazon's Leadership Principles, Meta's "move fast," Netflix's "freedom and responsibility"). Prepare core STAR stories, then adapt the framing to each company's stated values.
How much coding is expected in FAANG data science interviews? Usually SQL is mandatory; Python/R coding depth varies by team — some emphasize statistical modeling code, others focus more on analysis and communication than software engineering skill.
What's the hardest part of the loop for most candidates? Experimentation design — explaining how you'd set up an A/B test's primary metric, guardrails, sample size, and how you'd interpret an ambiguous or null result.
How important is business/product sense for a "pure" data scientist role? Very important at FAANG scale — even highly technical roles are evaluated on whether your analysis translates into a clear, actionable business recommendation.
Should I prepare differently for Amazon vs. Meta vs. Google? Keep the technical prep identical; adjust only the behavioral stories to reflect each company's specific cultural language, and research recent product launches relevant to the team you're interviewing for.
What's a good way to practice the SQL/stats screen? Timed practice under interview conditions matters more than untimed studying — simulate the pressure of writing a correct query or explaining a statistical concept out loud in under 5 minutes.
Related Reads
- FAANG Software Engineer Interview Questions
- FAANG Product Manager Interview Questions
- Google Data Scientist Interview Questions
- Meta Data Scientist Interview Questions
- Amazon Data Scientist Interview Questions
Practice the Full Loop End to End
InterviewBoost's mock interview mode simulates the full FAANG data science loop — SQL, experimentation, and behavioral — so you walk in having already done the reps, and live interview assist backs you up on the real calls.