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Design Push-Notification System for Airport Surge Pricing

Last updated: Mar 29, 2026

Quick Overview

Design Push-Notification System for Airport Surge Pricing evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  • medium
  • Upstart
  • Machine Learning
  • Data Scientist

Design Push-Notification System for Airport Surge Pricing

Company: Upstart

Role: Data Scientist

Category: Machine Learning

Difficulty: medium

Interview Round: Technical Screen

##### Scenario Airport surge pricing push notifications: deciding which drivers to notify when supply < demand ##### Question How would you design a push-notification ranking system that determines how many drivers to target and which drivers to include when airport surge pricing occurs? Why could a simple distance-radius rule perform poorly, and what improvements would you propose? Besides ETA, which additional features or metrics would you engineer to decide whether to send a notification? If neighborhood supply-demand imbalance may be predictive, how would you detect whether a region is imbalanced enough to trigger a notification? ##### Hints Discuss feature engineering, predictive modeling, supply-demand signals, real-time data, model evaluation.

Quick Answer: Design Push-Notification System for Airport Surge Pricing evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

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|Home/Machine Learning/Upstart

Design Push-Notification System for Airport Surge Pricing

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Upstart
Aug 4, 2025, 10:55 AM
mediumData ScientistTechnical ScreenMachine Learning
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Design Push-Notification System for Airport Surge Pricing

Designing Airport Surge Push Notifications for Drivers

Context

You are building a real-time system for a ride-hailing platform. When an airport experiences a surge (passenger demand exceeds available drivers), the system should decide:

  • How many drivers to notify (the "budget").
  • Which specific drivers to notify (the "ranking").

Assume you have real-time telemetry for drivers, trips, and demand forecasts, and you can send push notifications with per-driver throttling.

Task

  1. Outline a system to determine how many drivers to target and which drivers to include when airport surge pricing occurs.
  2. Explain why a simple distance-radius rule (e.g., notify anyone within 10 miles) can perform poorly, and propose improvements.
  3. Besides ETA, list additional features/metrics you would engineer to decide whether to send a notification.
  4. If neighborhood-level supply–demand imbalance is predictive, describe how you would detect whether a region is imbalanced enough to trigger a notification.

Hints

  • Discuss feature engineering, predictive modeling, supply–demand signals, real-time data, and model evaluation.

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

  • Clarify the task, data shape, labels, constraints, and evaluation metric.
  • State assumptions behind the math or modeling technique you choose.
  • Connect theory to practical training, debugging, and deployment implications.

What a Strong Answer Covers

  • Correct definitions and formulas where the prompt requires them.
  • A practical explanation of how the method behaves on real data.
  • Trade-offs, failure modes, diagnostics, and mitigation strategies.
  • Evaluation choices that match the product or modeling objective.

Follow-up Questions

  • How would noisy labels, class imbalance, or distribution shift affect the answer?
  • What would you monitor after deployment?
  • Which baseline would you compare against first?
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