Lyft Driver Churn Analysis

A data analytics take-home asking the candidate to: (1) define and label driver churn (binary 0/1), (2) calculate churn rate from the provided data, (3) cluster drivers into churn-based segments, (4) propose interventions to reduce churn and assess business impact, and (5) develop a hypothesis to size the opportunity of churn reduction. Three relational CSV datasets are provided: driver onboarding dates, ride-level metrics, and ride event timestamps spanning April–June 2016.