How the Meta Data Scientist loop actually runs — SQL, product sense, experimentation, and behavioral — with sample questions and the answer framework we drill with mentees.
Rounds
4–5 on-site
Screens
1 recruiter + 1 technical
Bar
L4 / IC4+
Time to prep
6–10 weeks
The loop, round by round
Meta's DS loop is remarkably standardized. Nail each round's bar and framework — they matter more than memorizing 200 questions.
1
SQL (technical screen + on-site)
The bar: Correct in ~15 min, talks through assumptions, uses window functions fluently without prompting.
Sample questions
For each user, find the second post they made after signup. Return user_id, post_id, and days_since_signup.
Given a table of Facebook Reels views, compute 7-day retention for a given cohort.
From an ads table, find advertisers whose spend dropped >30% week-over-week.
For each session, mark whether it contained a 'like' event that followed a 'view' event within 60 seconds.
Framework
Restate the schema and grain in your own words before writing. Choose window functions over subqueries when order matters. Call out NULL handling and ties explicitly — Meta interviewers grade on rigor, not brevity.
2
Product sense / analytical execution
The bar: Structured framework, chooses a north-star metric and 2 guardrails, and names at least one second-order effect.
Sample questions
Facebook Groups engagement dropped 8% last week. How do you investigate?
How would you measure the success of Instagram's 'Close Friends' story feature?
Should Meta launch a paid tier for WhatsApp Business? What data would you need?
Reels watch-time is up 20% but ad revenue is flat. What's happening?
Framework
Clarify → segment → hypothesize → metric → tradeoff. Spend 2 minutes clarifying the goal before proposing metrics. When investigating a drop, split by: seasonality, segment (country/platform/cohort), release timeline, upstream data health, sibling metrics.
3
Quant / experimentation
The bar: Can design a test end-to-end: hypothesis, unit of randomization, sample size intuition, guardrails, and a shutdown rule.
Sample questions
Design an A/B test for a new Reels ranking model. What's your primary metric and MDE?
You ran a 7-day test; the effect is +2% with p=0.06. Ship or not?
How would you handle network effects when testing a new Messenger feature?
A test shows +5% on the treatment metric and −1% on a guardrail. Walk me through the decision.
Framework
State the hypothesis in one sentence. Choose the smallest randomization unit that avoids interference (user > session > event). Name your MDE, power, and duration. Always define the shutdown rule and one counter-metric before launch.
4
Behavioral (aka 'jedi' round)
The bar: Concrete stories with metric outcomes, self-awareness, and one 'what I'd do differently'.
Sample questions
Tell me about a time your analysis changed a product decision.
Describe a conflict with a PM or engineer. How did you resolve it?
When have you pushed back on a leader?
Walk me through a project that failed. What did you learn?
Framework
STAR — Situation, Task, Action, Result — with the Result quantified. Meta specifically weights self-reflection: end every story with 'what I'd do differently'. Prep 6–8 stories that cover impact, conflict, ambiguity, and failure.
What Meta weights differently from Google / Amazon
Product sense is the tiebreaker. Meta DS roles sit inside product teams. A weak product round almost always tanks the loop, even with a perfect SQL score.
Experimentation depth over ML modeling. Unlike MLE loops, Meta DS rarely asks you to whiteboard a model. Expect deep A/B test design questions instead.
Behavioral is not a formality. The "jedi" round has veto power. Prep 6–8 stories, each with a quantified outcome and a self-reflection line.
Speed matters in SQL. Interviewers expect a correct answer inside 15 minutes with 5 minutes left for follow-ups. Practice against a timer.
Go deeper
Free Data Scientist reading guide + company-tagged Q&A on Pro
The free guide covers chapters 0–3 (foundations, SQL, experimentation, product sense). AfterKarma Pro adds full company-tagged interview questions for Meta, Google, Amazon and more — with mentor answers.
