Upstart Data Scientist interview questions typically reflect the company’s fintech focus: expect problems grounded in credit risk, model evaluation, causal inference and experimentation, plus practical coding and SQL work. Interviewers often evaluate statistical reasoning, machine‑learning intuition, ability to operationalize models, and how you communicate tradeoffs to product and risk partners. You should be ready for a mix of an initial recruiter screen, an online technical assessment (coding and stats), followed by several technical interviews and behavioral conversations that probe impact, ownership, and cross‑functional collaboration. For interview preparation, prioritize hands‑on practice: refresh Python and SQL coding, walk through end‑to‑end modeling case studies, and rehearse explaining metrics, feature choices, and validation strategies in plain language. Work on A/B testing and causal reasoning, and prepare concise STAR stories about projects where you drove measurable outcomes. During interviews, narrate your assumptions, demonstrate rigorous evaluation, and surface production and compliance considerations when relevant. This blend of technical depth and business clarity is what typically stands out.