Evaluate Push Notification Impact on Rideshare Supply Shortages

Q: How do I approach Analytics & Experimentation interview questions?

Analytics & Experimentation questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master analytics & experimentation interviews.

Q: What difficulty level is this interview question?

This is a hard difficulty Analytics & Experimentation question, commonly asked during Technical Screen rounds at Uber.

Q: What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Uber during technical interviews.

Question

Evaluate Push Notification Impact on Rideshare Supply Shortages

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:

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

Constraints & Assumptions

Preserve the scope, facts, inputs, and requested outputs from the prompt above.
If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.
Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.

Clarifying Questions to Ask Guidance

Clarify the business objective, unit of analysis, time window, exposure definition, and primary metric.
State assumptions about instrumentation, randomization, sample size, and data quality.
Separate descriptive analysis from causal claims.

What a Strong Answer Covers Guidance

A metric framework with primary, guardrail, and diagnostic metrics.
A credible analysis or experiment design with clear assumptions and bias checks.
SQL/statistical logic for segmentation, variance, confidence, and data validation where relevant.
An actionable recommendation that explains trade-offs and next steps.

Follow-up Questions Guidance

What sanity checks would you run before trusting the result?
How would you handle novelty effects, seasonality, or selection bias?
What decision would you make if metrics disagree?

Evaluate Push Notification Impact on Rideshare Supply Shortages

Quick Overview

Evaluate Push Notification Impact on Rideshare Supply Shortages

Evaluate Push Notification Impact on Rideshare Supply Shortages

Experiment Design: Push Notifications for Airport Surge Shortage Resolution

Context

Task

Constraints & Assumptions

Clarifying Questions to Ask Guidance

What a Strong Answer Covers Guidance

Follow-up Questions Guidance

Write your answer