Optimize Surge Notifications for Rideshare Drivers

Q: Optimize Surge Notifications for Rideshare Drivers

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.

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Question

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.

Optimize Surge Notifications for Rideshare Drivers

Scenario

Task

Solution

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