Meta Data Scientist Interview Guide

Meta Data Scientist interview reports consistently emphasize applying analysis to product decisions. Several candidates describe a technical screen that combines SQL and product sense, sometimes using a shared scenario or dataset across both parts. Reported SQL work includes retention analysis, joins, subqueries, window functions, and multi-step query logic.

Product cases focus on evaluating features and interpreting trade-offs. Candidates describe discussing group calling, recommendation changes, and feed or Reels engagement. Start with the decision you need to make, then define a success metric, identify meaningful guardrails, and explain what result would change your recommendation. Experiment-design follow-ups can cover randomization, power, novelty effects, segmentation, and the implications of a declining guardrail metric. For social products, be prepared to discuss how interactions between users can complicate treatment-control comparisons.

Loop structure varies across accounts. Multiple candidates explicitly report four loop interviews, including SQL, behavioral discussion, and case-oriented or analytical conversations; statistics or probability may arise in the loop. Treat reported topic mixes as preparation signals rather than a fixed script. Clear assumptions, a structured approach, and concise reasoning are recurring themes across the accounts.