Develop Dynamic-Pricing Algorithm for Lyft Balancing Key Factors
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.
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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