Design Dynamic Pricing System for Lyft: Key Features & Models

Q: Design Dynamic Pricing System for Lyft: Key Features & Models

This question evaluates a candidate's ability in dynamic pricing and real-time machine learning system design, focusing on demand/supply forecasting, price elasticity modeling, multi-objective optimization, fairness and safety constraints, feature engineering from streaming and historical data, and online experimentation.

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Question

Scenario

You are designing Lyft's real-time dynamic-pricing system to jointly optimize rider experience and marketplace health. The system should adjust prices at fine spatial and temporal resolution while accounting for demand spikes, driver availability, and regulatory/fairness constraints.

Key outcomes to balance:

Rider ETA (wait time)
Rider conversion (request and completion probability)
Driver earnings and utilization
Company revenue/GMV

Task

Design a dynamic pricing algorithm. Describe:

Features and data sources you would use in real time and historically.
The modeling components and overall architecture (e.g., forecasting, elasticity, policy/optimization; tree models vs. RL/bandits).
How you would balance trade-offs across objectives, including surge caps, fairness constraints, and safety guardrails.
How you would evaluate and monitor the system online.

Notes

Consider real-time demand/supply, location grid, time-of-day, historical price elasticity, driver proximity/ETA, weather/traffic, and special events.
Discuss model choices such as gradient-boosted trees vs. RL/bandits, and when to use each.
Include guardrails (e.g., surge caps, rate limits, regional fairness) and an experimentation/safety plan.

Design Dynamic Pricing System for Lyft: Key Features & Models

Scenario

Task

Notes

Solution

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