Meta Data Scientist interview typically runs 5 rounds: recruiter screen, SQL/Python technical screen, ML coding, product case study, and behavioral. The process spans 2–3 months and is distinguished by a single-scenario format where all rounds build on one problem domain.
$122K
Avg. Base Comp
$315K
Avg. Total Comp
4-5
Typical Rounds
6-10 weeks
Process Length
What separates candidates who advance at Meta from those who don't isn't usually the opening answer — it's what happens next. A recurring pattern across our candidates' experiences is that interviewers will deliberately remove your primary data source, flip the scenario, or push you into an edge case you didn't anticipate. The WhatsApp group calling case is the clearest example: candidates were asked to identify demand signals, then immediately told Instagram data wasn't available. The pivot to second-order signals — call clustering patterns, group chat activity within 30 minutes of a call, cross-geography call behavior — is exactly what Meta is evaluating. Candidates who treated each follow-up as a fresh question, rather than a continuation of the same analytical thread, consistently reported losing the thread of the conversation.
The SQL bar is higher than most candidates expect, and the CoderPad environment — where you cannot execute queries — removes the safety net most people rely on. Multiple candidates reported being hit with medium-to-hard SQL questions immediately, with no warm-up. Window functions and CTEs are table stakes; what Meta is actually watching is whether you clarify requirements before writing a single line. At least one candidate had to rewrite their query mid-interview after the interviewer restated what they were looking for — a painful but avoidable situation.
For the ML and product analytics rounds, the candidate who received an offer described the hardest moments as the ones where the opening prompt was vague and the real challenge unfolded through follow-up discussion. Structure matters more than the perfect answer. The candidate who spent 60 hours preparing and did mock interviews with a current Meta staff DS put it plainly: interviewers are drilling into formulas, variable selection, and interpretation — not just whether you can name the right metric. Knowing why you'd choose precision over recall in a fraud detection context, or how to handle SUTVA violations in a network experiment, is the difference between passing and not.
Synthetized from 16 candidates reports by our editorial team.
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Real interview reports from people who went through the Meta process.
I went through a fairly technical interview process for a Data Scientist role at Meta that centered on SQL, Python, and one ML-focused coding round. The first technical round I had was an advanced SQL and Python interview that felt more demanding than a standard Glassdoor-style screen. The SQL portion used a books schema and was less about simple querying and more about building logic step by step, handling data cleaning, and writing something efficient. The Python part was also LeetCode-style, so I had to think through the solution carefully rather than just recognize a pattern. Another round was similar in spirit, with standard Python and SQL questions, again emphasizing logic building over the sheer number of questions. The most unusual round was an ML coding interview that lasted 60 minutes and included two LeetCode problems I had to finish within the time limit. That made the pace pretty intense, because there wasn’t much room to get stuck on one problem. After that, I had a more conversational interview that was mostly a chat about my research background and interests in the role. That part was much lighter technically, but it still felt important for showing fit and explaining what kind of work I wanted to do. Overall, the process was challenging mainly because of the combination of advanced SQL, Python problem solving, and time pressure in the coding round. It was less about memorizing trivia and more about being able to reason clearly through multi-step problems. I didn’t get an offer in the end. If you’re preparing, I’d focus on complex SQL problems with a schema, Python LeetCode practice, and being able to explain your logic cleanly while coding under time pressure.
Prep tip from this candidate
Practice advanced SQL using multi-table schemas (like a books database), focusing on multi-step logic building and data cleaning rather than simple queries. For the ML coding round, simulate a strict 60-minute timer with two LeetCode problems back-to-back, since finishing both within the limit is part of the evaluation.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Meta
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| 2nd Highest Salary | |
| Comments Histogram | |
| Employee Salaries | |
| Merge Sorted Lists | |
| Liked Pages | |
| Last Transaction | |
| Session Difference | |
| Experiment Validity | |
| Random SQL Sample | |
| Like Tracker | |
| Flight Records | |
| Largest Salary by Department | |
| Emails Opened | |
| Swipe Precision | |
| Decreasing Comments | |
| Longest Streak Users | |
| Scrambled Tickets | |
| Recurring Character | |
| Lazy Raters | |
| Avg Friend Requests By Age Group | |
| Digital Library Borrowing Metrics | |
| WAU vs Open Rates | |
| Find Bigrams | |
| One Element Removed | |
| Fill None Values | |
| Impression Reach | |
| Search Ranking | |
| Network Experiment Design | |
| Promoting Instagram |
Synthesized from candidate reports. Individual experiences may vary.
Initial outreach from a Meta recruiter to discuss your background, available roles, and next steps. Candidates who applied through the portal often waited several weeks before hearing back, and the recruiter may present multiple role options before scheduling technical rounds.
A virtual 1-on-1 interview typically split into two parts anchored to a single problem domain (e.g., fraud detection, group calls, notifications). Part 1 covers medium-to-hard SQL queries (joins and window functions) and metric definition; Part 2 is a product sense or ML case study requiring end-to-end problem structuring, experiment design, and model evaluation. Conducted via CoderPad where queries cannot be executed.
A dedicated SQL interview with three to four questions requiring window functions and multi-step logic on a given schema. Emphasis is on reasoning through complex queries clearly, handling edge cases, and making assumptions explicit rather than simple pattern recognition.
Two case study interviews — one more conceptual and one more applied — covering product analytics, A/B testing design, network effects, metric definition, and statistics or probability questions. Interviewers probe deeply with follow-up scenarios, so structured thinking and clear prioritization of metrics matter more than perfect answers.
A structured behavioral interview covering topics such as handling constructive feedback, managing stakeholder communication, navigating changing project requirements, and describing the scale and impact of past work. Standard STAR-format preparation is expected.