Measure Impact of Updated Rider ETA Algorithm
Company: Uber
Role: Data Scientist
Category: Analytics & Experimentation
Difficulty: hard
Interview Round: Technical Screen
##### Scenario
A ride-hailing company just updated its rider ETA-prediction algorithm and wants to quantify the marketplace impact.
##### Question
Design an experiment to measure how the new rider-ETA model affects the business.
Which primary success metrics would you track on the demand and supply sides?
What intermediate or leading indicators would you monitor?
Would you pick a classic A/B test or a synthetic-control approach? Explain the design, randomization unit, duration, and how you would interpret results.
##### Hints
Map customer → marketplace funnel, define guardrail KPIs, consider network interference, seasonality, and statistical power before choosing between parallel A/B and geo-level synthetic control.
Quick Answer: Uber data scientist experimentation prompt on measuring an updated rider ETA model, covering demand and supply metrics, ETA calibration, marketplace interference, A/B tests, synthetic controls, guardrails, and rollout interpretation.