Detect Fraudulent Refund Requests for a Mid-Market Marketplace
Overview
What this challenge is about.
Detect Fraudulent Refund Requests for a Mid-Market Marketplace. Intermediate challenge in analysis. Analyzing real datasets and building models that drive de...
The Brief
What you'll do, and what you'll demonstrate.
Build a calibrated fraud-detection model whose operating point matches the trust & safety team's manual-review capacity.
This is not a data exercise. It is the work an analyst does when stakeholders need answers from messy data. That distinction matters to every hiring manager who has seen candidates describe statistical methods and none who have extracted insight from messy, real-world data.
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
- Handle a heavily imbalanced classification problem with appropriate techniques
- Use probability calibration (Platt scaling, isotonic regression) and reliability diagrams
- Tie operating points to real operational constraints
- Communicate trade-offs to a non-technical operations team
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.
- Classification
Apply classification to solve real industry problems and demonstrate production-level capability.
- Model Calibration
Apply model calibration to solve real industry problems and demonstrate production-level capability.
- Imbalanced Classification
Apply imbalanced classification to solve real industry problems and demonstrate production-level capability.
- Feature Engineering
Apply feature engineering to solve real industry problems and demonstrate production-level capability.
- Model Evaluation
Apply model evaluation to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Data Scientist
Calibrated classifiers tied to operational capacity are exactly the kind of work that junior data scientists own at marketplaces, fintechs, and trust & safety teams.
This challenge sharpens
- classification
- model-calibration
- model-evaluation
Applied AI Scientist
Choosing operating points based on real ops capacity instead of pure metrics is what separates applied work from research and is a daily applied-AI-scientist task.
This challenge sharpens
- model-calibration
- imbalanced-classification
- model-evaluation
AI Engineer
Packaging a calibrated model with a defensible threshold recommendation is the kind of glue work AI engineers do when handing models to operations teams.
This challenge sharpens
- python
- feature-engineering
- model-calibration