Pinterest Analytics & Experimentation interview questions focus on your ability to turn product hypotheses into credible, measurable decisions. At Pinterest the emphasis is often on experimentation at scale: defining clear success metrics, designing robust A/B tests, handling instrumentation and sampling quirks, and diagnosing metric movements across cohorts and days-in. Expect a mix of statistical rigor (power, confidence intervals, multiple testing, sequential analysis), practical SQL and Python data wrangling, and product-facing case discussions that evaluate tradeoffs between velocity, user experience, and measurement fidelity. For effective interview preparation, practice end-to-end experiments: formulate hypotheses, pick guardrail and primary metrics, compute sample size and MDE, run analyses in SQL/Python, and translate results into clear recommendations with uncertainty bounds. Be ready to discuss edge cases like novelty decay, metric leakage, and correlated metrics, and to explain how you’d instrument and monitor experiments in production. Communicating tradeoffs to engineers and product partners and proposing safe rollout strategies are often as important as the numbers themselves.