How do I approach Machine Learning interview questions?

Machine Learning questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master machine learning interviews.

What difficulty level is this interview question?

This is a hard difficulty Machine Learning question, commonly asked during Technical Screen rounds at Uber.

What role is this question designed for?

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

Optimize Surge Notifications for Rideshare Drivers

Quick Overview

This question evaluates a data scientist's competency in designing ML-driven real-time ranking and decision systems, including feature engineering, proxy estimation for ETA, metric definition, fairness considerations, and trade-off analysis for marketplace supply–demand matching.

Scenario

A rideshare marketplace experiences airport demand spikes. When demand exceeds supply, the system can send surge-pricing push notifications to nearby drivers to entice them to reposition toward the airport.

Task

List the business pros and cons of sending surge-pricing push notifications to nearby drivers.
Design a ranking system that decides how many drivers to notify and which drivers to target. State the objective, constraints, and the core features/signals your system would use.
Explain why a simple radius-based approach is inadequate, and propose data-driven improvements.
Propose a proxy for driver ETA to the airport (if full routing is unavailable), define the metrics you would compute to evaluate the system, and justify them.
Name additional real-time and historical metrics that should influence which drivers receive the push.
If neighborhood supply–demand imbalance is a feature, describe how to detect and quantify such imbalance.

Assume push notification latency needs to be low (sub-seconds to a few seconds) and consider feature engineering, real-time signals (supply, demand, distance), fairness, and offline evaluation.

Quick Overview

Task

List the business pros and cons of sending surge-pricing push notifications to nearby drivers.

Design a ranking system that decides how many drivers to notify and which drivers to target. State the objective, constraints, and the core features/signals your system would use.

Explain why a simple radius-based approach is inadequate, and propose data-driven improvements.

Propose a proxy for driver ETA to the airport (if full routing is unavailable), define the metrics you would compute to evaluate the system, and justify them.

Name additional real-time and historical metrics that should influence which drivers receive the push.

If neighborhood supply–demand imbalance is a feature, describe how to detect and quantify such imbalance.

Assume push notification latency needs to be low (sub-seconds to a few seconds) and consider feature engineering, real-time signals (supply, demand, distance), fairness, and offline evaluation.

Optimize Surge Notifications for Rideshare Drivers

Quick Overview

Scenario

Task

Solution

Comments (0)

Optimize Surge Notifications for Rideshare Drivers

Quick Overview

Scenario

Task

Solution

Comments (0)