Overview
What this challenge is about.
Learn a causal DAG from fintech risk data using hill-climbing and BIC, compare to a baseline, and write a CRO note. Earn a verifiable certificate.
The scenario
The fintech (Series C, around 320 staff, processes about USD 1.8B in annual SME flows) is under regulator pressure to show its risk decisioning is auditable and structured, not just a tree of opaque rules.
The Brief
What you'll do, and what you'll demonstrate.
Learn the structure of a probabilistic graphical model over fintech risk signals and identify edges that should change the team's mental model.
Earning criteria — what you'll demonstrate
- Apply score-based (hill-climbing + BIC) and constraint-based (PC) structure learning
- Compare learned DAGs quantitatively (held-out log-loss) and structurally (Hamming distance)
- Interpret graphical-model edges in causal-but-careful language
- Communicate probabilistic findings to a C-suite audience
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.
- Structure Learning
Apply structure learning to solve real industry problems and demonstrate production-level capability.
- Bayesian Networks
Apply bayesian networks to solve real industry problems and demonstrate production-level capability.
- Causal Modeling
Apply causal modeling 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.
- Executive Communication
Apply executive communication to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
ML Researcher
Comparing score-based and constraint-based structure learning on real risk data is genuine applied-research work and a strong portfolio piece for ML research roles.
This challenge sharpens
- structure-learning
- bayesian-networks
- causal-modeling
Applied AI Scientist
Turning a learned graphical model into three concrete belief updates for the CRO mirrors the daily craft of applied AI scientists in regulated industries.
This challenge sharpens
- bayesian-networks
- executive-communication
- model-evaluation
Research Scientist
The structure-learning comparison plus bootstrap stability analysis is the level of rigor research-scientist roles in industry expect from a first project.
This challenge sharpens
- structure-learning
- causal-modeling
- model-evaluation