Refit a Pricing Model for an Insurance Comparison Site
Overview
What this challenge is about.
Refit a pricing model with elastic-net and top-K interactions, compare results, and deliver a ship/no-ship memo for a verifiable certificate.
The scenario
The comparison site (around 90 staff, around EUR 30M annual revenue from referral fees) has a model the product team can no longer easily reason about; the head of pricing wants a refit that is both better and more interpretable.
The Brief
What you'll do, and what you'll demonstrate.
Refit a pricing-relevant click model to improve log-loss without regressing calibration, and recommend ship or no-ship.
Earning criteria — what you'll demonstrate
- Apply elastic-net regularization to a real product model
- Search interactions in a principled (not ad-hoc) way
- Run temporal back-tests with bootstrap CIs
- Defend a ship/no-ship recommendation in writing
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Statistical Machine Learning
Master · Machine Learning
Strong alignment
This challenge maps to Statistical Machine Learning at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Regularized Regression
Apply regularized regression to solve real industry problems and demonstrate production-level capability.
- Feature Interactions
Apply feature interactions to solve real industry problems and demonstrate production-level capability.
- Calibration
Apply calibration to solve real industry problems and demonstrate production-level capability.
- Model Comparison
Apply model comparison to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
- Bootstrap Analysis
Apply bootstrap analysis 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:
Data Scientist
Refitting a production model with a defensible methodology and ship/no-ship memo is the classic senior data-science task in fintech.
This challenge sharpens
- regularized-regression
- feature-interactions
- calibration
Machine Learning Engineer
Temporal back-testing with bootstrap CIs is the same discipline MLEs apply to broader production-model rollouts.
This challenge sharpens
- model-comparison
- bootstrap-analysis
- calibration
Applied AI Scientist
Owning a model refit end-to-end including the leadership memo is the daily reality of applied-AI scientists in conversion-driven product orgs.
This challenge sharpens
- regularized-regression
- calibration
- model-comparison