Meta’s Data Scientist interviews target candidates who can turn large-scale product data into clear, measurable product decisions. Expect a blend of technical and product-focused assessments: Meta Data Scientist interview questions often probe SQL and Python data manipulation, statistical inference and A/B test design, metric definition and instrumentation, and product sense around engagement and growth. Distinctive to Meta is the emphasis on scale, experimentation, and the ability to communicate actionable insights to engineers and product managers; interviewers typically evaluate both analytical rigor and storytelling clarity. The process usually begins with a recruiter screen, moves to one or more technical screens (coding/SQL plus a product or metrics case), and culminates in a loop of interviews that combine analytics, research-design, and behavioral rounds. For effective interview preparation, prioritize timed practice on data manipulation problems, refresh hypothesis testing and power intuition, rehearse product-metric case studies aloud, and craft concise STAR stories that emphasize measurable impact. Complement technical practice with mock interviews and clear explanations of tradeoffs so you can translate analyses into product recommendations under time pressure.