Overview
What this challenge is about.
Build a contextual-bandit simulator, compare exploration strategies via IPS, and recommend a live A/B test strategy to earn a verifiable certificate.
The scenario
The startup (Series A, around 40 staff) sees a 25 percent drop-off after the first 3 recommendations and believes the current epsilon-greedy with epsilon=0.1 underexplores fresh content.
The Brief
What you'll do, and what you'll demonstrate.
Offline-evaluate three exploration strategies for a meditation-app recommender and recommend one for the next live A/B.
Earning criteria — what you'll demonstrate
- Implement epsilon-greedy, Thompson sampling, and UCB1 from scratch
- Apply inverse propensity scoring for off-policy evaluation
- Reason about exploration-exploitation trade-offs on real production logs
- Translate offline-evaluation results into an A/B test design
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Reinforcement Learning
Master · Reinforcement Learning
Strong alignment
This challenge maps to Reinforcement 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.
- Contextual Bandits
Apply contextual bandits to solve real industry problems and demonstrate production-level capability.
- Thompson Sampling
Apply thompson sampling to solve real industry problems and demonstrate production-level capability.
- Ucb
Apply ucb to solve real industry problems and demonstrate production-level capability.
- Off Policy Evaluation
Apply off policy evaluation to solve real industry problems and demonstrate production-level capability.
- Exploration
Apply exploration 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
Offline-evaluating exploration strategies on a real recommender log is the day-one job of growth-leaning data scientists at consumer-AI startups.
This challenge sharpens
- contextual-bandits
- off-policy-evaluation
- exploration
Machine Learning Engineer
Implementing and testing three exploration strategies and shipping the winner to a live A/B is core MLE work in recommender teams.
This challenge sharpens
- thompson-sampling
- ucb
- python
Applied AI Scientist
Trading off exploration, fairness, and long-tail coverage is the kind of judgement applied AI scientists bring to ranking and recommendation problems.
This challenge sharpens
- contextual-bandits
- exploration
- off-policy-evaluation