Airbnb Data Scientist interview questions typically probe both technical depth and product impact: expect live Python coding and SQL (joins, window functions, CTEs), experiment design and statistics, applied machine learning trade-offs, plus a take‑home or project presentation. What’s distinctive about Airbnb’s loop is its marketplace focus—interviewers often evaluate your ability to tie models and analyses to concrete business metrics (supply/demand, pricing, conversion funnels) and to reason about biases, causality, and operational constraints while collaborating across product and engineering teams. For interview preparation, prioritize three threads: technical fluency (SQL, pandas/numpy, model evaluation), experiment and product sense (A/B test design, metric definition, diagnostic thinking), and storytelling (clear presentation of a take‑home or past project, plus strong STAR behavioral examples). Practice full “data loops” end‑to‑end: frame the business question, outline the analysis or model, defend assumptions and tradeoffs, and translate findings into actionable recommendations. Familiarize yourself with Airbnb’s values and be ready to explain impact, cross‑functional collaboration, and decisions under uncertainty.