How many rounds are in the Meta Data Scientist interview loop?

The full loop is typically 4–5 rounds following a recruiter screen and one technical phone screen. The virtual onsite includes a product sense/metrics round, a SQL coding round, a statistics and experiment design round, and a behavioral round. Some teams add a fifth round focused on machine learning or a second SQL session.

What SQL topics does Meta test data scientists on?

Expect intermediate-to-hard SQL: window functions (LAG, LEAD, RANK, DENSE_RANK), CTEs, self-joins, conditional aggregations, and time-series analysis. Common themes include funnel analysis, retention cohorts, DAU/MAU calculations, and feature adoption metrics — all in a product data context.

What is a 'metric movement' question at Meta?

A metric movement question gives you a scenario like 'DAU dropped 10% last Tuesday — walk me through how you'd investigate.' You are expected to systematically rule out data pipeline issues, external events, and product changes before proposing hypotheses, then suggest the SQL or analysis to confirm each hypothesis.

How does Meta evaluate experiment design for data scientists?

Meta expects you to design an A/B test end-to-end: define the hypothesis, choose a randomization unit, calculate sample size (given significance level α = 0.05 and power ≥ 0.8), identify guardrail metrics, and flag threats like novelty effect, network interference, or Simpson's paradox. Concrete familiarity with Meta's scale — billions of users — matters.

What levels does Meta hire data scientists at, and what is the compensation?

Meta uses IC levels. IC3 (new grad or early career) has a total compensation near $169K. IC4 (mid-level, roughly 3–5 years experience) reaches approximately $269K total. IC5 (senior) is around $420K total, and IC6 (staff) around $590K total. Figures from levels.fyi as of late 2025 and include base, RSUs, and bonus.

What behavioral values does Meta probe in the interview?

Meta's cultural values include Move Fast, Focus on Long-Term Impact, and Be Direct. Interviewers listen for evidence that you act with urgency, make decisions with imperfect data, push back constructively on stakeholders, and can explain the real-world impact of your work in numbers.

Is machine learning tested in the Meta Data Scientist interview?

It depends on the track. Product Analytics Data Scientists (the most common track) are evaluated primarily on SQL, metrics, and experiment design — not ML modeling. Applied Research Scientist or ML-heavy DS roles do test model selection, evaluation, and deployment. Check the job description to identify which track you are interviewing for.

How long does the Meta Data Scientist interview process take?

From recruiter contact to final decision, expect four to eight weeks. The recruiter screen and technical phone screen are typically scheduled within two weeks of first contact. The virtual onsite loop is usually one to two weeks after that. Post-loop feedback and offer decisions generally take one to two weeks.

Meta Data Scientist Interview Questions (2026)

How many rounds are in the Meta Data Scientist interview loop?: The full loop is typically 4–5 rounds following a recruiter screen and one technical phone screen. The virtual onsite includes a product sense/metrics round, a SQL coding round, a statistics and experiment design round, and a behavioral round. Some teams add a fifth round focused on machine learning or a second SQL session.
What SQL topics does Meta test data scientists on?: Expect intermediate-to-hard SQL: window functions (LAG, LEAD, RANK, DENSE_RANK), CTEs, self-joins, conditional aggregations, and time-series analysis. Common themes include funnel analysis, retention cohorts, DAU/MAU calculations, and feature adoption metrics — all in a product data context.
What is a 'metric movement' question at Meta?: A metric movement question gives you a scenario like 'DAU dropped 10% last Tuesday — walk me through how you'd investigate.' You are expected to systematically rule out data pipeline issues, external events, and product changes before proposing hypotheses, then suggest the SQL or analysis to confirm each hypothesis.
How does Meta evaluate experiment design for data scientists?: Meta expects you to design an A/B test end-to-end: define the hypothesis, choose a randomization unit, calculate sample size (given significance level α = 0.05 and power ≥ 0.8), identify guardrail metrics, and flag threats like novelty effect, network interference, or Simpson's paradox. Concrete familiarity with Meta's scale — billions of users — matters.
What levels does Meta hire data scientists at, and what is the compensation?: Meta uses IC levels. IC3 (new grad or early career) has a total compensation near $169K. IC4 (mid-level, roughly 3–5 years experience) reaches approximately $269K total. IC5 (senior) is around $420K total, and IC6 (staff) around $590K total. Figures from levels.fyi as of late 2025 and include base, RSUs, and bonus.
What behavioral values does Meta probe in the interview?: Meta's cultural values include Move Fast, Focus on Long-Term Impact, and Be Direct. Interviewers listen for evidence that you act with urgency, make decisions with imperfect data, push back constructively on stakeholders, and can explain the real-world impact of your work in numbers.
Is machine learning tested in the Meta Data Scientist interview?: It depends on the track. Product Analytics Data Scientists (the most common track) are evaluated primarily on SQL, metrics, and experiment design — not ML modeling. Applied Research Scientist or ML-heavy DS roles do test model selection, evaluation, and deployment. Check the job description to identify which track you are interviewing for.
How long does the Meta Data Scientist interview process take?: From recruiter contact to final decision, expect four to eight weeks. The recruiter screen and technical phone screen are typically scheduled within two weeks of first contact. The virtual onsite loop is usually one to two weeks after that. Post-loop feedback and offer decisions generally take one to two weeks.

Meta receives hundreds of applications for every data scientist opening. The median total compensation for a senior-level (IC5) role sits around $420,000 per year — well above the national median of $112,590 for all data scientists tracked by the Bureau of Labor Statistics — which partly explains the competition. The interview process is deliberately structured to filter for a specific skill set: product intuition built on top of rigorous statistical thinking, all communicated at the speed of a company that ships at a billion-user scale.

Understanding exactly what Meta tests in each round — and why — is the fastest path to a credible prep plan.

The Meta interview loop: stage by stage

Recruiter screen (30 minutes). A phone or video call to confirm your background, discuss team fit, and explain the process. The recruiter is already calibrating level — IC3, IC4, or IC5 — based on how you describe scope of impact and independence in past roles. Prepare a tight two-minute summary of your most recent data science project that includes the business question, your methodology, and the outcome in numbers.

Technical phone screen (45–60 minutes). One live session with a data scientist on the team. This is usually a combined test: a SQL problem in a shared editor, followed by a product or metrics conceptual question. Strong SQL execution here is the primary gate. Candidates who struggle with window functions or struggle to explain what a query is doing are not advanced regardless of how well the metrics conversation goes.

Virtual onsite loop (4–5 rounds, each 45 minutes). This is the main event. The rounds are typically spread across one or two days. Here is the standard structure:

Product sense and metrics. You are given a scenario — a new feature, a metric that moved, a tradeoff between two success definitions — and asked to reason through it aloud. This round evaluates whether you think like a product partner, not just an analyst.
SQL coding. A harder SQL problem than the phone screen. Expect multi-step queries that combine window functions, CTEs, and conditional aggregations in a product data context. You write in a live editor with the interviewer watching.
Statistics and experiment design. A/B testing end-to-end: hypothesis formulation, randomization unit, sample size calculation, guardrail metrics, and result interpretation. You may also be asked probability questions or asked to explain the difference between confidence intervals and p-values in plain language.
Behavioral / cross-functional. Structured behavioral questions mapped to Meta’s cultural values. One or two stories per question, told with specificity and quantified outcomes.
Optional fifth round. Some teams include a machine learning round (model framing, evaluation) or a second SQL and analytics session. The hiring manager round, where it exists, blends a few behavioral questions with a discussion of team direction.

Post-loop debrief. Interviewers submit independent written assessments before the team debrief. Unlike Amazon, Meta does not use a Bar Raiser system — decisions are made by the hiring team with input from a calibration process. Offer decisions typically take one to two weeks after the onsite.

What Meta uniquely evaluates

Most large tech companies test SQL and statistics. What separates a competitive Meta candidate from a technically sufficient one is the ability to move fluidly between data and product reasoning — and to do it fast.

Product instinct as a first-class skill. Meta interviews data scientists as product partners, not as back-office analysts. The product sense round is not a warmup — it is a primary signal. Interviewers probe whether you can define the right metric before you know how to measure it, whether you understand what makes a metric gameable, and whether you can reason about second-order effects on user behavior. Candidates who only practice SQL and statistics and neglect product thinking regularly fail at this stage.

Metric movement diagnosis. Meta has specific frameworks it expects candidates to apply when a metric changes unexpectedly. A good answer to “DAU dropped 10% this week” does not start with a hypothesis — it starts with a data pipeline check. You need to confirm the data itself is sound before theorizing about product causes. After ruling out instrumentation issues, you move to external factors (seasonality, outages, competitor events), then to recent product changes. Structuring your answer this way signals operational maturity.

Experimentation at scale. Meta runs thousands of concurrent experiments across billions of users. Questions about experiment design probe whether you understand network interference (a user in control sees friends in treatment, contaminating results), novelty effect (early engagement spikes that decay), and the difference between randomizing at the user level versus the session or device level. You are expected to reason about these without a prompt — if the interviewer has to feed you these concepts during the interview, it counts against you.

Direct communication under pressure. Meta’s cultural value “Be Direct” is not rhetorical. Behavioral interviewers are specifically listening for whether you pushed back on a stakeholder when you believed they were wrong, whether you delivered an uncomfortable finding to a senior leader, and whether you can articulate disagreements crisply. Vague answers that hedge every claim are penalized more at Meta than at most other large tech companies.

SQL: what to practice and at what depth

Meta SQL questions in the onsite are typically set in a product analytics context — user events, feature interactions, ad delivery logs. The complexity is not about obscure syntax; it is about translating a product question into a multi-step query correctly under time pressure.

Common patterns:

Retention analysis. Given a table of user events with timestamps, compute 7-day and 30-day retention. Requires self-joins or window functions with date arithmetic.
Funnel analysis. Identify users who completed step A but not step B within a time window. Requires conditional aggregation or CASE WHEN logic combined with a window function.
DAU/MAU ratio. Compute engagement rate over a rolling 28-day window per user segment. Requires window functions with a ROWS BETWEEN clause.
Ranking within groups. Find the top-N content items by engagement for each user segment. Requires RANK() or DENSE_RANK() inside a PARTITION BY.

Sample question: “We have a table user_events(user_id, event_type, event_date). Write a query to find all users who were active in week 1 of January 2025 but not in week 2.”

A strong answer uses a CTE to define week 1 active users, a second CTE for week 2, and a LEFT JOIN ... WHERE week2.user_id IS NULL or a NOT IN subquery. Interviewers note whether you handle NULLs correctly and whether your SQL is readable.

Statistics and experiment design: sample answers

Q: How do you calculate the sample size required for an A/B test?

A solid answer covers four inputs: baseline conversion rate, minimum detectable effect (MDE — the smallest change worth detecting), significance level (α, typically 0.05), and desired power (1 - β, typically 0.80). The practical rule of thumb is n ≈ 16 × (variance / δ²), where δ is the expected difference between treatment and control. Then explain that you would validate the duration against Meta’s traffic volume to ensure you can run the test long enough to avoid peeking problems — and that you would set a fixed end date before launching to prevent optional stopping.

Q: You ran an A/B test. The primary metric is flat but a secondary metric improved. What do you conclude?

Do not call it a success. First, check whether the secondary metric improvement survives a Bonferroni correction for multiple comparisons. Second, ask whether the secondary metric is a leading indicator of the primary metric or a separate dimension of quality. Third, consider whether the flat primary metric hides a heterogeneous treatment effect — the improvement might be concentrated in a subgroup that is being offset by degradation elsewhere. Present your interpretation as a hypothesis, not a conclusion, and recommend a follow-up experiment.

Behavioral round: mapping stories to Meta’s values

Meta interviewers probe three values with particular frequency for data scientist candidates:

Move Fast. Evidence: a time you shipped an imperfect analysis that was still good enough to unblock a product decision, rather than waiting for perfect data. The story should show judgment about when to act versus when to investigate further.

Focus on Long-Term Impact. Evidence: a time you pushed back on a short-term metric optimization because it conflicted with a longer-term user or business outcome. This is a chance to show product partnership — you cared about what the metric was actually measuring.

Be Direct. Evidence: a time you delivered a finding or a recommendation that a stakeholder did not want to hear. The story should show you were respectful but clear, and that you had data to back the position.

Prepare two to three specific stories per value. For each story, use the STAR structure (Situation, Task, Action, Result) and end with a concrete result — percentage improvement, time saved, revenue impact, or decision that was changed because of your analysis. Using “I” instead of “we” throughout is important; interviewers need to credit your contribution specifically.

Level and compensation context

Meta’s data scientist levels map to IC (Individual Contributor) bands. Most new-graduate hires enter at IC3 with total compensation around $169,000. Candidates with three to five years of industry experience typically interview for IC4, with total comp around $269,000. IC5 (senior data scientist) requires demonstrated cross-functional scope — leading analyses that changed product direction — and pays roughly $420,000 total. IC6 (staff data scientist) involves driving strategy for a major product area and commands around $590,000 in total compensation. All figures are from levels.fyi aggregated data as of late 2025 and reflect base salary, annual RSU vesting, and target bonus combined.

The level is often decided during the recruiter screen based on resume scope. If you believe you are being slotted too low, ask the recruiter directly. Leveling affects which interviewers you are paired with and how the bar is calibrated — an IC3 candidate who gives IC5-caliber answers may get an offer at a higher level than originally budgeted.

How to prep: a four-week plan

Week 1 — SQL fluency. Solve 20–25 window function and aggregation problems on DataLemur or StrataScratch, specifically those tagged Meta or Facebook. Focus on writing correct SQL in one pass rather than iterating. Get comfortable with CTEs; Meta interviewers appreciate readable, modular code.

Week 2 — Experiment design depth. Work through the end-to-end A/B test design framework until you can do it from memory: hypothesis, unit of randomization, metric selection, sample size formula, threats to validity, result interpretation. Practice explaining it aloud in under five minutes without notes.

Week 3 — Product intuition. Practice the metric movement framework on real Meta products — News Feed, Instagram Reels, WhatsApp, Ads. For each, ask: what are the primary and secondary metrics, what would a 10% drop in the primary metric suggest, what would you check first? Reviewing Meta’s public earnings call commentary helps calibrate what metrics the company actually cares about.

Week 4 — Behavioral polish and mock interviews. Write out two to three STAR stories per Meta value. Practice delivering them in under three minutes each. Do at least one full mock interview with a peer or coach so you get feedback on pacing and precision under pressure.

Throughout prep, track your progress and organize your prep materials systematically. Managing parallel interview pipelines — recruiter screens, take-homes, onsite scheduling — adds logistical overhead that compounds stress during an already demanding process. Using a dedicated job tracker to centralize all touchpoints, deadlines, and follow-up tasks is one of the simplest ways to make sure nothing slips while you are deep in SQL practice.

The data science job market is growing fast — BLS projects 34% employment growth for data scientists from 2024 to 2034, faster than nearly any other occupation. Meta remains one of the most sought-after employers in the field. The interview is hard, but it is learnable. The candidates who get offers are rarely the ones with the deepest mathematical background; they are the ones who prepared specifically for how Meta thinks about data.