Meta Data Scientist interview typically runs 4 rounds: recruiter screen, technical SQL screen, product case, behavioral. Timeline is about 1 week; it is fast-paced and highly structured.
$160K
Avg. Base Comp
$294K
Avg. Total Comp
4-5
Typical Rounds
3-5 weeks
Process Length
We’ve seen a very consistent pattern in Meta’s Data Scientist interviews: the company cares less about whether you can produce a correct answer and more about whether you can frame the problem cleanly, choose the right metric, and defend your logic under pressure. Multiple candidates reported that the hardest part wasn’t the SQL itself, but the follow-up discussion around why that query or metric mattered. In one experience, the interviewer even restated the question midstream because the candidate had jumped too quickly into execution without clarifying the ask. That’s a recurring theme across the experiences we reviewed — Meta is testing whether you can stay structured when the prompt evolves.
Another clear signal is how often the cases revolve around social products with messy real-world dynamics: group calls, bad content detection, stolen content, friend requests, notifications, and network effects. Our candidates repeatedly ran into questions where the obvious metric was not enough. For example, several people were pushed on contamination, spillovers, and SUTVA violations when designing experiments for connected users. Others were asked to think through guardrails like unsubscribes, retention, or DAU when a feature improved one metric but hurt another. That tells us Meta is looking for candidates who understand trade-offs in social systems, not just dashboard movement.
The non-obvious make-or-break factor is depth of reasoning. A few candidates said the interviewer was perfectly happy to keep probing as long as the answer stayed coherent, but would quickly lose patience if the candidate couldn’t connect product behavior to measurement. We’ve also seen that Meta likes to anchor questions in a single dataset or feature and then keep tightening the scope with follow-ups. If you can move from signal identification to experiment design to interpretation without losing the thread, you’re speaking the language Meta seems to reward.
Synthetized from 20 candidates reports by our editorial team.
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Real interview reports from people who went through the Meta process.
The first round was a technical screen, and it moved fast. I had about 5 minutes for introductions, then roughly 30 minutes of SQL, 30 minutes of Python, and a few minutes at the end for Q&A. They let me choose whether to start with SQL or Python, which was nice, but the pace was still intense because in each section I was expected to get through up to five questions and fully pass the test cases before moving on. It felt less like a discussion and more like a timed coding checkpoint, so I had to think and write very quickly. The bar seemed pretty clear: if you don’t get to at least 3 out of 5 in each section, you’re probably not moving forward.
The SQL questions were the most memorable part for me because they were practical and product-oriented rather than purely academic. One of the questions was about identifying users who made multiple posts within their first week, and I had to think through it while also talking about how I’d measure the impact on retention. That style carried into the rest of the process too. Later rounds were organized around analytical reasoning, product sense, metrics, A/B testing, and trade-offs, plus a behavioral round where I had to walk through a project or situation in depth and keep answering follow-up questions. There was also a SQL-heavy technical round that felt like it could be solved with an AI assistant, but the real test was whether I could explain the logic and stay structured. Overall, the interviews were rigorous and very organized, and the main takeaway for me was that Meta really wants depth of thinking, not just correct answers. I ended up not getting an offer, so I’d definitely recommend practicing under time pressure and having a clear framework for experimentation and diagnosis questions.
Prep tip from this candidate
Practice timed SQL and Python screens where you need to fully pass each test case before moving on, and get comfortable answering product-style SQL questions like retention or first-week activity. For the later rounds, rehearse a clear structure for metrics, A/B testing, and trade-off questions so you can explain your reasoning quickly under pressure.
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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 | |
| Experiment Validity | |
| Liked Pages | |
| Last Transaction | |
| Session Difference | |
| Random SQL Sample | |
| Search Ratings | |
| Like Tracker | |
| Flight Records | |
| Largest Salary by Department | |
| Average Order Value | |
| Emails Opened | |
| Swipe Precision | |
| Top 3 Users | |
| Decreasing Comments | |
| Impression Reach | |
| Longest Streak Users | |
| Bank Fraud Model | |
| Scrambled Tickets | |
| Recurring Character | |
| Lazy Raters | |
| Closed Accounts | |
| WAU vs Open Rates | |
| Network Experiment Design | |
| Digital Library Borrowing Metrics | |
| Liked and Commented |
Synthesized from candidate reports. Individual experiences may vary.
An initial recruiter call to review your background, discuss the role, and confirm fit for the Data Scientist track. In some experiences, the recruiter also shared multiple role options and helped route candidates into a product analytics or product data scientist loop.
A fast-paced first technical round that typically combines SQL with product sense or a short ML/data science case. Candidates reported medium-to-hard SQL questions in CoderPad or a shared editor, plus follow-ups on metrics, experiment design, and how to evaluate a feature or model.
One loop interview is usually dedicated to deeper SQL or coding work, often with multiple questions in a single session. The questions can include window functions, joins, complex schema logic, Python problem solving, or LeetCode-style coding under time pressure.
A case-style round focused on product metrics, experimentation, and trade-offs. Common themes include social features like group calls, bad content detection, network effects, launch decisions, and how to design A/B tests with the right primary, secondary, and guardrail metrics.
A round centered on statistical reasoning and investigation. Candidates described questions on metric distributions, CLT, probability, and diagnosing changes in product metrics, often with follow-up questions that test structured thinking and root-cause analysis.
A behavioral interview covering past projects, stakeholder management, handling feedback, and communicating through ambiguity or changing requirements. Interviewers often probe for depth with follow-up questions about impact, collaboration, and decision-making.