Roblox Data Scientist interview questions tend to emphasize product-driven analysis at game scale: expect problems that combine SQL and Python data manipulation, statistical reasoning for experimentation, and modeling that connects directly to player engagement and monetization. What’s distinctive is the mix of gaming-domain nuance (sessionization, event streams, retention funnels) with platform-scale concerns (large event volumes, real-time pipelines, and feature-store thinking). Interviewers are looking for clear assumptions, pragmatic trade-offs, and the ability to translate analysis into product decisions. For interview preparation, balance technical drills with product storytelling. Practice writing concise, efficient SQL and Python code on realistic event tables, refresh hypothesis testing and causal inference for A/B tests, and prepare STAR stories that highlight ownership, cross-functional collaboration, and measurable impact. Expect an initial recruiter screen, a technical phone loop with coding and statistics, and a deeper onsite series covering modeling, product-case design, and behavioral fit. Time your answers, state assumptions up front, and prioritize clarity—being able to explain why an analysis matters to players and the business is often as important as getting the right number.