Overview
What this challenge is about.
Build a loan default model from 18 months of transaction data and compare three ML approaches. Earn a verifiable certificate.
The scenario
The fintech (around 180 staff) serves about 4,000 SME sellers across Singapore, Indonesia, and Vietnam; lending is a USD 8M revenue opportunity but a botched first cohort could exhaust the lending facility within a quarter.
The Brief
What you'll do, and what you'll demonstrate.
Ship a default-risk model with a defensible proxy label and an honest model card the credit committee can underwrite the first cohort against.
Earning criteria — what you'll demonstrate
- Engineer features from transaction time-series for a tabular ML model
- Compare model families honestly with a temporal (not random) split
- Calibrate probabilities and explain the difference between rank and probability
- Write a model card that supports a real-money decision
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.
- Feature Engineering
Apply feature engineering to solve real industry problems and demonstrate production-level capability.
- Model Selection
Apply model selection 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.
- Calibration
Apply calibration to solve real industry problems and demonstrate production-level capability.
- Ml Pipelines
Apply ml pipelines 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:
Machine Learning Engineer
Building a tabular risk model end-to-end with proper temporal evaluation and a model card is the day-one job of a junior MLE at a fintech or risk-modeling team.
This challenge sharpens
- feature-engineering
- model-evaluation
- ml-pipelines
Data Scientist
Defining a proxy label, comparing model families, and writing the decision memo for a credit committee is the core data-science loop in lending.
This challenge sharpens
- model-selection
- calibration
- feature-engineering
Applied AI Scientist
Translating a modeling result into a real-money underwriting decision with an honest model card is exactly what applied AI scientists do in regulated industries.
This challenge sharpens
- model-evaluation
- calibration
- feature-engineering