Overview
What this challenge is about.
Exploration Strategies for a Recommendation Bandit. Advanced challenge in analysis. Analyzing real datasets and building models that drive decisions, earn a ...
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.
This is not a data exercise. It is the work an analyst does when stakeholders need answers from messy data. That distinction matters to every hiring manager who has seen candidates describe statistical methods and none who have extracted insight from messy, real-world data.
When you finish, you will have something most graduates do not: a real-world deliverable, verified by Ewance, that you can show to a hiring manager and say "I did this. Here is the proof."
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