Expect Chime Data Scientist interview questions to blend fintech product thinking with core data science fundamentals: interviewers often probe SQL and Python fluency, experimental design and causal thinking, model selection and evaluation, and the ability to translate analyses into product metrics and member impact. Distinctive to Chime is a practical, product-forward orientation—candidates are evaluated not just on algorithms but on how their work moves business metrics, communicates with cross‑functional partners, and prioritizes member outcomes. Communication, measurement rigor, and a bias toward simple, reliable solutions are commonly assessed alongside technical correctness. In practice you should anticipate a recruiter screen, a technical/SQL coding screen, at least one product or case-style conversation, and behavioral interviews focused on ownership and collaboration; many stages are virtual. For interview preparation, prioritize clean SQL problem solving, clear explanations of model tradeoffs, a few quantified project stories using STAR-style structure, and mock product cases tied to retention, activation, or fraud metrics. Review Chime’s product flows so your recommendations land in context, and practice communicating tradeoffs and uncertainty concisely.