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 Lyft.

What role is this question designed for?

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

Develop Dynamic-Pricing Algorithm for Lyft Balancing Key Factors

Last updated: Mar 29, 2026

Quick Overview

This question evaluates proficiency in dynamic pricing and production machine learning, covering feature engineering, real-time demand and supply forecasting, elasticity estimation and causal inference, model selection (forecasting, optimization, bandits/RL), experimentation, and operational guardrails for ride-hailing platforms.

Lyft

Jul 12, 2025, 6:59 PM

Data Scientist

Technical Screen

Machine Learning

Scenario

You are designing Lyft’s real-time dynamic-pricing system ("surge") to balance rider demand, driver supply, and company revenue while meeting service-quality and fairness constraints.

Task

Propose the features, data sources, and modeling approach for Lyft’s dynamic-pricing model. Explain how you would balance the trade-offs among demand, supply, fairness, and revenue. Include how you would estimate elasticity, choose/compose models, set constraints/guardrails, and validate the system.

Guidance

Discuss:

Real-time supply and demand signals.
Historical trends and seasonality.
Context: weather, traffic, events, geography.
Elasticity signals and causal estimation.
Model choice (e.g., demand/supply forecasting + optimization, bandits/RL) and constraints.
Experimentation, metrics, and operational safeguards.

Solution

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