Design a Multi-Objective Dynamic Pricing Engine for CityRide
Overview
What this challenge is about.
Design a Multi-Objective Dynamic Pricing Engine for CityRide. Expert-level challenge in design. Designing real products under real constraints, earn a blockc...
The Brief
What you'll do, and what you'll demonstrate.
Design a dynamic pricing algorithm that sets real-time fares to optimize a weighted objective across platform revenue, rider acceptance, and driver utilization, outperforming the current static surge policy.
This is not a design exercise. It is the work a product designer does between a brief and a shipped interface. That distinction matters to every hiring manager who has seen candidates redesign Spotify's homepage and none who have worked under real product constraints.
When you finish, you will have something most graduates do not: a real-world deliverable, verified by Ewance, that you can show to a hiring manager and say "I did this. Here is the proof."
Earning criteria — what you'll demonstrate
- Formulate real-time pricing as a multi-objective optimization or sequential-decision problem with explicit, defensible weights
- Engineer features from trip, weather, and events history to forecast demand and price sensitivity
- Build a simulation that fairly compares a proposed policy against an existing baseline and quantifies uncertainty
- Translate model trade-offs into business recommendations a non-technical executive can act on
- Communicate technical results through clear, decision-oriented visualizations
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Reinforcement Learning
Apply reinforcement learning to solve real industry problems and demonstrate production-level capability.
- Optimization
Apply optimization to solve real industry problems and demonstrate production-level capability.
- Simulation
Apply simulation to solve real industry problems and demonstrate production-level capability.
- Time Series Forecasting
Apply time series forecasting to solve real industry problems and demonstrate production-level capability.
- Data Visualization
Transform complex data into clear, insightful visual representations.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Pricing / Marketplace Data Scientist
Marketplace and pricing teams need scientists who can turn supply-demand data into revenue decisions. This challenge mirrors that work end to end: you build the model, prove it beats a baseline in simulation, and defend the trade-offs to leadership.
This challenge sharpens
- optimization
- simulation
- data-visualization
Decision Scientist (Mobility & Logistics)
Mobility and logistics firms run dynamic systems where prices and supply interact in real time. Here you practice forecasting demand and reasoning about sequential decisions under uncertainty, exactly the judgment these roles reward.
This challenge sharpens
- reinforcement-learning
- time-series-forecasting
- simulation
Applied ML Engineer (Real-Time Systems)
Real-time ML roles demand models that are both accurate and deployable. This challenge bridges to that work by asking you to prototype a live-scoring engine and propose how it would consume signals and be A/B tested in production.
This challenge sharpens
- reinforcement-learning
- optimization
- time-series-forecasting