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Evaluate Push Notification Impact on Rideshare Supply Shortages

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a data scientist's ability to design and analyze experiments for causal impact measurement, assessing competency in metric selection, appropriate randomization units, spillover and contamination management, and statistical inference.

  • hard
  • Uber
  • Analytics & Experimentation
  • Data Scientist

Evaluate Push Notification Impact on Rideshare Supply Shortages

Company: Uber

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: hard

Interview Round: Technical Screen

##### Scenario Rideshare airport surge pricing push notifications need rigorous measurement of causal impact ##### Question Design an experiment to evaluate the effectiveness of the push notification on resolving supply shortages. Specify success metrics, driver-level A/B setup, how to handle spill-over effects, and how you will establish causality. ##### Hints Think cluster randomization, geo-split, CUPED, difference-in-difference, guard-rail metrics, and contamination checks.

Quick Answer: This question evaluates a data scientist's ability to design and analyze experiments for causal impact measurement, assessing competency in metric selection, appropriate randomization units, spillover and contamination management, and statistical inference.

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Uber logo
Uber
Aug 4, 2025, 10:55 AM
Data Scientist
Technical Screen
Analytics & Experimentation
17
0

Experiment Design: Push Notifications for Airport Surge Shortage Resolution

Context

When rider demand at the airport exceeds available driver supply, surge pricing activates. The product team wants to send push notifications to nearby eligible drivers prompting them to go to the airport to alleviate the shortage. You need to measure the causal impact of these notifications on resolving shortages.

Task

Design an experiment to evaluate the effectiveness of the push notification on resolving airport supply shortages. Address the following:

  1. Success metrics (primary, secondary, and guardrails).
  2. Driver-level A/B setup and the recommended randomization unit.
  3. How to handle spillover and contamination effects across geographies and time.
  4. How you will establish causality and analyze results (e.g., CUPED, difference-in-differences, cluster-robust inference).

Solution

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