Lyft Data Scientist Interview Guide

Lyft Data Scientist candidates in these reports should prepare to turn statistical knowledge into clear product decisions. One candidate described a recruiter screen followed by a 45-minute phone interview with a data scientist. That conversation combined motivation and prior-experience prompts with a metric-diagnosis exercise, a business-oriented binomial problem, and probability questions. The same report also named churn, coupon behavior, driver conversion experiments, distributions, p-values, and Bayesian inference.

A second candidate received an offer after an interview focused on product analytics, experimentation, and machine learning for advertising. Their questions covered designing an A/B test for an ad-ranking change, choosing success metrics and significance criteria, investigating a CTR decline after launch, and monitoring advertising-platform health beyond CTR. Follow-up discussion included confounding, cohort segmentation, and explaining correlation versus causation to a product manager. They were also asked to describe a data science project that influenced a product decision.

Practice explaining the diagnostic path, not just the final metric: validate inputs, segment results where relevant, weigh experiment evidence, and communicate a recommendation to cross-functional partners. The available reports are limited, so treat the reported sequence as one candidate’s experience rather than a universal format.