Meta Data Scientist Interview Guide

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.