MCMC for Conversion-Funnel A/B Testing at a Marketplace
Overview
What this challenge is about.
Fit a Bayesian MCMC model to marketplace funnel A/B test data, then write a roll/kill memo to earn a verifiable certificate.
The scenario
The marketplace (Series B, around 140 staff, USD 65M Annual Recurring Revenue) needs roll/kill calls on funnel experiments within a week to keep its growth roadmap moving; ambiguous results currently sit for a month or more.
The Brief
What you'll do, and what you'll demonstrate.
Reanalyze a funnel A/B test with hierarchical Bayesian MCMC to give growth a defensible per-stage roll/kill recommendation.
Earning criteria — what you'll demonstrate
- Build a Bayesian hierarchical model with partial pooling across groups
- Run NUTS MCMC and verify convergence with standard diagnostics
- Interpret posterior probabilities for business decisions
- Communicate Bayesian results to a frequentist-trained growth 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.
- Mcmc
Apply mcmc to solve real industry problems and demonstrate production-level capability.
- Bayesian Hierarchical Models
Apply bayesian hierarchical models to solve real industry problems and demonstrate production-level capability.
- Ab Testing
Apply ab testing to solve real industry problems and demonstrate production-level capability.
- Convergence Diagnostics
Apply convergence diagnostics to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
- Growth Analytics
Apply growth analytics 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
Hierarchical Bayesian A/B reanalysis is exactly the work growth-focused data scientists do when frequentist tests stall.
This challenge sharpens
- mcmc
- bayesian-hierarchical-models
- ab-testing
Applied AI Scientist
Choosing priors, validating MCMC convergence, and translating posteriors into a roll/kill recommendation is core applied-research work.
This challenge sharpens
- mcmc
- convergence-diagnostics
- bayesian-hierarchical-models
ML Researcher
Defending hierarchical-model choices and prior sensitivity in writing prepares a student for research roles where methodological transparency matters.
This challenge sharpens
- bayesian-hierarchical-models
- mcmc
- convergence-diagnostics