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.
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:
Design a dynamic pricing algorithm. Describe:
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