Snapchat Data Scientist interview questions focus on product-driven analytics at scale: expect SQL and Python fluency, rigorous A/B testing and experiment-design questions, plus applied machine learning or modeling scenarios framed around user engagement, ad performance, and real-time social features. What’s distinctive is the product emphasis — interviewers assess your ability to translate messy event data into clear product metrics, reason about causal inference and rollout risk, and propose actionable experiments that move engagement or revenue. Technical depth is balanced with communication and trade‑off judgement. For interview preparation, practice medium-to-hard SQL problems, refresh statistical inference and experiment diagnostics, and rehearse end-to-end product case studies where you define metrics, identify biases, and recommend interventions. Prepare concise walk-throughs of past projects that highlight business impact and code or analysis samples. Timebox mock screens to polish clear, data‑driven storytelling and be ready to discuss scaling, latency, and data-quality tradeoffs when modeling user behavior.