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Meta Data Scientist Interview Questions

Meta DS roles split into 'Core DS' (research-heavy, ML) and 'Analytics DS' (product analytics + experimentation). Interview differs for each. This guide covers Analytics DS.

Process length
6-10 weeks
Rounds
7
Questions
8
Mid-level TC
$240k–$330k (IC4)
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The Meta Data Scientist interview process

What to expect, in order.

  1. 1Recruiter screen (30 min — fit + level)
  2. 2Technical screen (60 min — SQL + stats fundamentals)
  3. 3Onsite — typically 4-5 rounds
  4. 4Stats / Experimentation round (60 min)
  5. 5SQL / Data manipulation round (60 min)
  6. 6Product analytics case (60 min — metric movement diagnosis)
  7. 7Behavioral / cross-functional round (60 min)

What Meta actually evaluates

Meta Analytics DS are tightly partnered with PMs and Engineers. Ship insights weekly, run dozens of A/B tests, own metrics dashboards. Interview rewards quick movement between analysis and recommendation.

Move fast — ship insights weekly
Be bold — recommendations even with imperfect data
Focus on impact — quantified business outcomes per quarter
Be open — share data widely, iterate publicly

Process quirks worth knowing

Meta uses 'be the hero' framing in product analytics rounds — define metric movement so clearly the PM feels it. Then your hypothesis feels obvious. Failure mode: jumping to root cause before establishing the pattern crisply.

8 questions Meta actually asks

Each question includes the tip for answering and what the interviewer is actually evaluating.

Q1case

Daily active users on Instagram dropped 4% yesterday. How do you investigate?

Why Meta asks: Canonical Meta DS question. Decomposition tree before root-cause hypotheses.
How to answer: Decomposition tree: segment (new vs existing), geo, platform (iOS/Android), entry source. Check external events (outages, holidays, competitor launches). State top 2-3 hypotheses with confirmation data.
What they evaluate: Structured decomposition, multiple hypotheses, false positive awareness
Q2technical

Write a SQL query to find users who first posted in week N and then posted again in week N+1.

How to answer: Self-join the events table with week aliases. Filter for: user's first post (MIN(date) GROUP BY user) in target week, then EXISTS subquery for second post in following week. Use date_trunc('week', date) for boundaries.
What they evaluate: Self-join comfort, date arithmetic, week-boundary edge cases
Q3case

How do you design an A/B test for a new feed ranking algorithm at Meta?

Why Meta asks: Deep experimentation knowledge. Meta runs hundreds of tests in parallel — interaction effects matter.
How to answer: Cover: randomization unit (user? session?), primary metric (engagement + counter-metric for value), sample size, novelty effect handling, interaction with concurrent tests. Discuss bucket-vs-holdout.
What they evaluate: Experimental design rigor, Meta-scale awareness, counter-metric thinking
Q4technical

What's the difference between a leading and lagging indicator? Give examples for Meta products.

How to answer: Leading = predicts future outcome (e.g., daily active days in first week predicts 30-day retention). Lagging = reports past outcome (e.g., monthly revenue). Discuss tradeoffs: leading is noisier but faster.
What they evaluate: Conceptual clarity, Meta-specific examples, tradeoff awareness
Q5behavioral

Tell me about a time your analysis changed a product decision.

Why Meta asks: Meta values impact-driven analysis. Pure 'I found X' stories without action fail.
How to answer: Pick real example. Lead with the decision your analysis changed. Then context, your analysis, the resistance, outcome. Quantify business impact.
What they evaluate: Genuine business impact, ability to influence, quantified outcome
Q6technical

Explain p-value to a non-technical PM.

How to answer: Avoid 'probability the null is true' (common misinterpretation). Better: 'if we ran this test 1000 times assuming the feature has zero impact, in how many would we see results as extreme as ours?'. Then translate to ship/no-ship.
What they evaluate: Conceptual clarity, audience-aware communication, statistical nuance
Q7case

How would you measure the success of WhatsApp Channels?

How to answer: Layered: north star (creators × subscribers), input (channel creation rate, post frequency), engagement (subscribers per channel, message open rate), retention (cohort 14-day return). Counter-metric: spam channels.
What they evaluate: Two-sided marketplace fluency, counter-metric awareness, business framing
Q8values

Why Meta specifically vs Google or Amazon for DS roles?

How to answer: Connect to Meta's specific challenges (social graph, news feed ranking, content moderation), pace ('move fast'), or specific bets (Reels, WhatsApp Business, AI). Show Meta-specific DS work understanding.
What they evaluate: Genuine Meta-specific interest, pace alignment, multi-year intent

Common ways candidates fail this interview

Specific to Meta, not generic interview advice.

  • ⚠️Vague metric stories without quantified business impact
  • ⚠️Strong SQL but weak stats — Meta DS need both equally
  • ⚠️Jumping to root cause before establishing the pattern
  • ⚠️Treating product analytics like pure stats — Meta wants action-oriented insights
  • ⚠️Generic 'I want to work on AI' — Meta wants specific team and product interest

Meta Data Scientist compensation (2026)

Entry / Junior
$180k–$230k total comp (IC3)
Mid-level
$240k–$330k total comp (IC4)
Senior+
$360k–$520k total comp (IC5)

Sources: levels.fyi, Glassdoor, public filings (US figures, total compensation including base + bonus + equity).

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