Analyze Causes of Increased Lyft Ride Wait Times
Overview
This question evaluates a data scientist's abilities in root-cause analysis, causal inference, pricing strategy, and experimentation design applied to ride-hailing wait-time metrics.
Lyft
Aug 4, 2025, 10:55 AM
Data Scientist
Technical Screen
Analytics & Experimentation
118
0
Scenario
A ride-hailing marketplace observes a 20% month-over-month increase in rider wait time (time from request to driver arrival).
Tasks
- Root-cause analysis: How would you structure an analysis to identify the drivers of the 20% wait-time increase?
- Pricing assessment: Assuming pricing may be a contributing factor, how would you evaluate the costs and benefits of moving from static to dynamic pricing?
- Experimentation: How would you design an experiment to validate whether dynamic pricing reduces ETA while controlling for demand–supply interference?
Clarifications and minimal assumptions
- Wait time/ETA = time from rider request to driver pickup.
- The marketplace is two-sided with riders (demand) and drivers (supply).
- Static pricing = fixed per-minute/per-mile with occasional promos; dynamic pricing = price multipliers that respond to local demand–supply conditions.
- Interference means that treating a subset (e.g., riders) can affect outcomes for others (e.g., by moving drivers), violating SUTVA if not addressed.
Solution
Login required
Show