Develop Dynamic-Pricing Algorithm for Lyft Balancing Key Factors

Q: Develop Dynamic-Pricing Algorithm for Lyft Balancing Key Factors

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.

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Question

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.

Develop Dynamic-Pricing Algorithm for Lyft Balancing Key Factors

Scenario

Task

Guidance

Solution

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