Evaluate ETA Impact on Conversion
Quick Overview
This question evaluates a data scientist's skills in causal inference, experimental design, and marketplace analytics, including understanding of confounding, selection bias, interference, and the distinction between correlation and causation.
Uber
Feb 14, 2026, 12:00 AM
Data Scientist
Technical Screen
Analytics & Experimentation
6
0
You are a Senior Data Scientist at a ride-hailing company such as Uber. ETA refers to the estimated pickup time shown to a rider before they decide whether to request a trip.
A product manager wants to reduce ETA and asks you to evaluate the impact on the business.
Answer the following related questions:
- Why does reducing ETA matter? Discuss the expected rider, driver, and marketplace benefits of lowering ETA.
- In historical observational data, you notice that higher ETA is positively correlated with rider conversion . Why might this happen, even if longer waits are not truly causing higher conversion? Provide several plausible explanations.
-
How would you design an
experiment
to estimate the causal impact of lowering ETA on conversion?
- Define the treatment and control conditions.
- Choose the primary success metric and at least 2-3 guardrail metrics.
- Specify the randomization unit and explain when to measure exposure.
- Explain how you would compute and interpret a confidence interval for the treatment effect.
- Mention any power or minimum detectable effect considerations.
- Why might user-level randomization be a poor choice in this marketplace setting? What alternatives would you consider, and what tradeoffs do they introduce?
Your answer should explicitly discuss issues such as confounding, selection bias, marketplace interference, and the difference between correlation and causation.
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