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
24 practiced
You have logged delivery of dispatch actions and outcomes under the current production policy. Explain how you would use inverse propensity scoring (IPS) to estimate the expected reward of a new dispatch policy offline. Describe key assumptions, practical issues (propensity estimation, clipping), and one way to reduce variance.
EasyTechnical
30 practiced
List a concise pre-launch experiment sanity checklist you would run before starting any Lyft product A/B test. Include checks for randomization, instrumentation, sample-size, metric definitions, and logging. For each checklist item give a one-sentence reason why it matters.
HardTechnical
31 practiced
When randomized experiments are impractical, explain three methods to estimate long-term effects of a feature (e.g., loyalty program): structural models, agent-based simulations, and instrumental variables. For each method describe required assumptions, data needs, and a key robustness check you would run.
MediumSystem Design
22 practiced
Design an offline evaluation pipeline to compare multiple ETA prediction models for Lyft. Include the data ingestion (raw GPS/trip logs), label generation (actual travel time), feature recomputation, test sets (time-based splits), performance slices, drift detection, and SLA requirements for model acceptance. Describe core components and failure modes to watch for.
MediumTechnical
21 practiced
Case study: your monitoring shows matching latency increased by 35% in a major market and completed trips dropped by 2%. Outline a disciplined root-cause analysis plan combining product and ML perspectives: what logs to collect, experiments/AB tests to pause or rollback, and hypotheses to test (model drift, infrastructure, feature-store lag).
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