Janek Thomas
Senior Data Scientist
Munich, Bavaria, Germany
Summary
🤩
Rockstar🎓
Top SchoolJanek Thomas is a Senior Data Scientist based in Munich with 11 years of experience focused on Automated Machine Learning, hyperparameter optimization, feature selection and reproducible MLOps. He combines academic rigor—holding a PhD in Statistics and multiple university teaching and postdoctoral roles—with hands-on engineering, having led AutoML and XAI efforts at Fraunhofer and built cloud benchmarking pipelines at H2O.ai and OpenML. Janek has a track record of shipping production-ready tooling (Docker + AWS EC2 orchestration, benchmarking folds, infrastructure classes) and contributing learners and tests to the prominent mlr R ecosystem. He co-founded and ran a data science training company and served on the board of a local data community, demonstrating both entrepreneurial and community leadership. Known in open source circles for "AutoML is Life," he bridges research and software by turning AutoML research into maintainable, deployable systems.
11 years of coding experience
5 years of employment as a software developer
Dr. rer. nat. - PhD, Statistics, Dr. rer. nat. - PhD, Statistics at Ludwig-Maximilians-Universität München
English, German
Github Skills (23)
docker10
boto10
python10
classification10
r10
machine-learning10
learn2learn10
dockers10
learing10
learnr10
aws10
automl10
learndash10
learnpress10
testing9
Programming languages (10)
Github contributions (5)
openml/automlbenchmark
Aug 2018 - Jan 2022
OpenML AutoML Benchmarking Framework
Role in this project:
MLOps Engineer
MLOps EngineerContributions:1 review, 92 commits, 5 PRs in 3 years 5 months
Contributions summary:Janek's commits focus on building and deploying a framework for benchmarking AutoML systems within an OpenML context. They developed scripts for running docker containers on AWS EC2 instances, evaluating results, and integrating AutoML frameworks. Furthermore, the user created a class for managing the AWS infrastructure, incorporated console output results, and refactored the system to benchmark with folds, incorporating several improvements to existing code. These changes indicate a strong emphasis on automating the execution and evaluation of machine learning models on a cloud platform.
benchmarkingopenmlmachine-learningbenchmarkautoml
mlr-org/mlr
Mar 2016 - May 2019
Machine Learning in R
Role in this project:Data Scientist
Data ScientistContributions:60 commits, 74 PRs, 149 pushes in 3 years 2 months
Contributions summary:Janek contributed significantly to the machine learning aspects of the `mlr-org/mlr` repository, as evidenced by the addition of a new learner, `classif.C50`, and associated tests. The user also modified existing code related to sampling and wrapper parameters for machine learning models. Furthermore, the user removed code related to Density Preserving Sampling (DPS) and removed impurity importance from surv.ranger.
imbalance-correctionlearnersensemble-learningclassificationr-package
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