Hyperparameter Search via CMA-ES for a Pharma QSAR Model
Overview
What this challenge is about.
Tune 8 hyperparameters for a pharma QSAR model with CMA-ES, then compare methods and write a memo for a verifiable certificate.
The scenario
The startup (around 50 people, working with mid-cap pharma clients out of Basel and Boston) runs about 40 QSAR hyperparameter searches per week; a 20% reduction in evaluations saves roughly EUR 14,000 per month in GPU spend across the active projects.
The Brief
What you'll do, and what you'll demonstrate.
Pick the default hyperparameter optimizer for a pharma QSAR AutoML stack by fairly benchmarking CMA-ES, random search, and Bayesian optimization.
Earning criteria — what you'll demonstrate
- Apply CMA-ES to a real ML hyperparameter optimization problem
- Compare metaheuristic search to Bayesian and random baselines fairly
- Quantify search stability across seeds
- Communicate algorithm trade-offs to engineering leadership
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Evolutionary Computation and Metaheuristic Search
Master · Machine Learning
Strong alignment
This challenge maps to Evolutionary Computation and Metaheuristic Search 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.
- Cma Es
Apply cma es to solve real industry problems and demonstrate production-level capability.
- Metaheuristics
Apply metaheuristics to solve real industry problems and demonstrate production-level capability.
- Hyperparameter Optimization
Apply hyperparameter optimization to solve real industry problems and demonstrate production-level capability.
- Evaluation
Apply evaluation to solve real industry problems and demonstrate production-level capability.
- Benchmarking
Apply benchmarking 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:
Applied AI Scientist
Benchmarking optimizers under fair budgets and recommending a default for a production AutoML stack is exactly the work applied AI scientists do at pharma-AI firms.
This challenge sharpens
- cma-es
- hyperparameter-optimization
- benchmarking
Data Scientist
Fair experimental design, variance reporting, and a stakeholder-ready memo transfer directly to data-science roles on any modeling team.
This challenge sharpens
- evaluation
- hyperparameter-optimization
- python
ML Researcher
Stability analysis of search algorithms across seeds is the kind of methodology question ML researchers answer for product teams.
This challenge sharpens
- cma-es
- metaheuristics
- evaluation