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.
EasyBehavioral
0 practiced
Tell me about a time you convinced cross-functional stakeholders (PMs, engineers, ops) to run an experiment or delay a launch because of measurement, risk, or product concerns. Describe Situation, Task, Action, and Result, focusing on the data you used to influence the decision and the trade-offs you negotiated.
MediumSystem Design
0 practiced
Design a monitoring dashboard for live A/B experiments at Lyft. Specify the primary panels (OEC, key guardrails, per-segment effects), real-time checks (sample ratio mismatch, pre-period balance, SMDs), multiple-testing controls, early-warning flags, and the data freshness / latency requirements. Explain how a PM should interpret alerts and drill down to root causes.
EasyTechnical
0 practiced
Compute the minimum sample size per arm required to detect an absolute increase of 1.0 percentage point in ride conversion rate (from 5.0% to 6.0%) with 80% power using a two-sided z-test at alpha = 0.05. Assume equal randomization and independent users. Show the formula or Python code you would use and provide the numeric answer (rounded up).
MediumTechnical
0 practiced
An A/B test shows that displaying a shorter ETA on the request screen increased request conversion by 3% but decreased completed rides by 2%. As the analyzing Data Scientist, outline a concrete data-analysis plan to diagnose root causes, immediate mitigations, and design follow-up experiments that resolve the trade-off. List specific diagnostics and visualizations you'd produce.
EasyTechnical
0 practiced
Define the Stable Unit Treatment Value Assumption (SUTVA) and explain concrete ways SUTVA might be violated in Lyft experiments (for example: driver incentive changes, dispatch algorithm adjustments, or pricing changes). For each violation, propose at least one mitigation strategy you could implement during experiment design or analysis.
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