Lyft Business & Services Familiarity Questions
Familiarity with Lyft's business model, core products and services (ridesharing platform, mobility offerings, pricing strategy), partnerships, and market positioning as part of understanding the company's business and culture.
HardTechnical
26 practiced
Design an experiment to estimate network externalities: specifically, quantify how a percentage increase in active drivers in a region affects rider wait times, completion rates, and rider growth over a six-month horizon. Define experimental units, randomization plan, strategies for handling spillovers and buffer zones, and metrics for short- and long-term effects.
MediumTechnical
23 practiced
Design an attribution approach to assign Lyft rides to marketing touchpoints (email, push, paid channel, organic search). Discuss last-touch vs multi-touch models, probabilistic attribution, and causal alternatives (experiments, incremental lift testing). How would you evaluate which attribution model is most useful for budgeting?
EasyTechnical
24 practiced
List Lyft's main competitors in the North American ride-hailing and mobility space and describe one distinct strategic differentiator Lyft uses to position itself (could be product, partnerships, brand, or technical approach). Explain why this differentiator matters to Lyft's data science priorities.
MediumTechnical
23 practiced
Design a set of features for an ML model that predicts ETA for driver-to-pickup and pickup-to-dropoff legs. Consider GPS noise, route variability, time-of-day, traffic patterns, and holiday events. For each feature describe why it's useful and any preprocessing you would apply.
MediumTechnical
30 practiced
Explain an offline evaluation approach for a routing or dispatch policy change using logged policy data. Which counterfactual estimators (e.g., IPS, SNIPS), dataset requirements, and variance reduction techniques would you use to estimate expected improvement before running online tests?
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