Code
Designing a Dynamic Pricing Engine for a Ride-Hailing Startup
Overview
What this challenge is about.
Your team is given a dataset of historical rides (timestamp, pickup location zone, demand level, available drivers). You must design a pricing algorithm that: (1) uses a multiplier based on demand/supply ratio, (2) caps the multiplier to avoid extreme surges (max 2.5x), (3) simulates a day of operations with 15-minute intervals, and (4) outputs total revenue, average wait time, and number of unfulfilled rides. Success means your algorithm improves revenue by at least 10% over flat pricing while keeping wait times under 5 minutes. Constraints: use Python with pandas and numpy; present results in a 10-minute slide deck.
The Brief
What you'll do, and what you'll demonstrate.
How can CityGo implement a dynamic pricing strategy that maximizes revenue without significantly increasing customer wait times?
Earning criteria — what you'll demonstrate
- Write a Python simulation that models a real-time system with time intervals
- Apply data structures (DataFrames) to manage time-series data
- Implement a business rule (dynamic pricing formula) and evaluate its impact
- Collaborate in a team to divide tasks and integrate code modules
- Communicate technical results to a non-technical audience via slides
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Skills
Skills you'll demonstrate.
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Careers
Roles this prepares you for.
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