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.
HardTechnical
0 practiced
You want to measure the causal effect of introducing a driver-priority matching queue (high-rated drivers matched first) on marketplace fairness across neighborhoods. Describe an experimental or quasi-experimental design to estimate neighborhood-level effects, how you'd measure fairness, and how you'd detect spillovers across nearby neighborhoods.
EasyTechnical
0 practiced
In the context of Lyft product experiments, explain what statistical significance, p-value, and confidence interval mean. For each, provide an intuitive example (e.g., conversion rate change), describe common pitfalls in product decision-making (multiple comparisons, peeking), and state when you might prefer a Bayesian approach instead of classical p-values.
HardTechnical
0 practiced
You discover product stakeholders are pushing to ship a feature that you believe will degrade long-term marketplace health. Describe how you would influence the roadmap and align stakeholders: what evidence you'd collect, how you'd present trade-offs, escalation paths, and how you'd propose an iterative experiment or guardrail approach to de-risk the decision.
EasyTechnical
0 practiced
You're designing an A/B test to evaluate a new Lyft carpool feature that groups riders with similar routes. Describe how you'd choose the single primary metric and at least two guardrail metrics. Explain why each metric matters for rider experience, driver experience, and marketplace efficiency, and mention any potential short-term vs long-term trade-offs in choosing those metrics.
HardTechnical
0 practiced
Seasonality confounder: propose a method to estimate holiday-season uplift of a marketing promotion when seasonality affects all users. Discuss design options (holiday vs control windows, synthetic control, time-series decomposition), bias/variance trade-offs, and how you'd validate your estimate.
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