Stefan Coors
Expert Manager, Data Science - AI, Insights & Solutions (AIS)
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolStefan Coors is an expert manager in data science with a decade of experience blending research-grade statistical rigor and applied ML leadership at Bain & Company and LMU Munich. He leads AI and insights initiatives from San Francisco, driving production-ready solutions while supervising teams across advanced analytics and AutoML benchmarking. His hands-on background includes contributions to the popular mlr R library and implementing autoxgboost integration and time-budgeting features in the OpenML AutoML benchmark, showing deep practical knowledge of model tooling and reproducible evaluation. Stefan combines a strong academic foundation—ongoing PhD work in statistical learning and multiple advanced degrees in statistics and mathematical economics—with enterprise delivery experience, making him fluent at translating complex methods into business impact. A detail-minded engineer, he has a track record of improving threshold tuning, multiclass AUC measures and packaging R tools for robust deployment, reflecting both code-level craftsmanship and strategic product sense.
10 years of coding experience
7 years of employment as a software developer
Statistik, Statistik at Athens University of Economics and BusinessSummer School, Summer School at Tongji UniversityMaster of Science Wirtschaftsmathematik, Master of Science Wirtschaftsmathematik at Bielefeld UniversityMaster of Science (M.Sc.) Statistik, Master of Science (M.Sc.) Statistik at Ludwig-Maximilians-Universität MünchenMathematik, Mathematik at Queen's University Belfast
German, English
Github Skills (15)
mle10
xgboost10
machine-learning10
benchmark10
benchmarking10
automl10
mlr10
classification10
ml10
r10
python9
performance-tuning8
auto-tuning8
fine-tuning8
regression7
Programming languages (6)
Github contributions (5)
mlr-org/mlr
May 2016 - Apr 2019
Machine Learning in R
Role in this project:
Data Scientist
Data ScientistContributions:25 commits, 29 PRs, 83 pushes in 2 years 11 months
Contributions summary:Stefan contributed to the machine learning library, `mlr`, by modifying existing functions and adding new functionality. The commits show modifications to cost measure calculations, the addition of new learners, and the implementation of multiclass AUC measures. The user also addressed issues related to threshold tuning and made improvements to existing code, demonstrating a focus on enhancing the library's capabilities for various machine learning tasks.
imbalance-correctionlearnersensemble-learningclassificationr-package
openml/automlbenchmark
Aug 2019 - Jan 2022
OpenML AutoML Benchmarking Framework
Role in this project:ML Engineer
ML EngineerContributions:18 commits, 3 PRs, 4 comments in 2 years 5 months
Contributions summary:Stefan primarily contributed to the `autoxgboost` framework, which is designed for AutoML benchmarking. They implemented and refined the `exec.R` and `exec.py` scripts, integrating the autoxgboost model. The user added features such as time budgeting for model training and addressed bug fixes within the autoxgboost framework. They also made changes to the setup scripts to ensure proper installation and dependencies of R packages, making the integration of autoxgboost into the benchmark.
benchmarkingopenmlmachine-learningbenchmarkautoml
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