Lyft-Specific Product Problems & Analytical Approaches Questions
Lyft-specific product challenges, problem framing, hypothesis generation, and data-driven decision making, focusing on experimentation design, metrics, and feature prioritization within the ride-hailing and on-demand transportation context. Includes product discovery, A/B testing, funnel analysis, and stakeholder alignment to improve rider and driver experiences and marketplace efficiency.
MediumTechnical
0 practiced
Design an approach to compute 3-year driver Lifetime Value (LTV) for Lyft drivers. Describe revenue streams and costs to include, churn model choices, discounting, sensitivity analysis, and how to validate the model against observed data.
EasyTechnical
0 practiced
As a BI Analyst at Lyft, leadership asks you to produce a concise weekly marketplace health report. List and justify the top 6 metrics you would include (cover rider-side, driver-side, and marketplace-efficiency). For each metric explain: 1) what direction indicates improvement vs degradation, 2) one guardrail metric to watch alongside it, and 3) how often it should be monitored (real-time, daily, weekly).
HardTechnical
0 practiced
A complex SQL report now takes 10 minutes to run and blocks dashboards. Propose a prioritized set of strategies to optimize performance: query refactor, indexing/partitioning, materialized views, pre-aggregations, denormalization, or moving to OLAP. Explain pros/cons and when to choose each approach.
EasyTechnical
0 practiced
Explain the core concepts of A/B testing relevant to Lyft experiments: define control vs treatment, explain randomization and balance, describe the purpose of guardrail metrics, and list three guardrail metrics you would always monitor for a rider-facing UI experiment and why.
HardTechnical
0 practiced
Design an identification strategy for an experiment that changes driver incentives and simultaneously affects rider demand. Explain how you would separate supply-side effects (more drivers online) from demand-side effects (riders requesting more rides) and estimate the equilibrium impact on ETAs and revenue.
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