Jakob Richter
Head Of Data Science
Berlin, Germany
Summary
🤩
Rockstar🎓
Top SchoolJakob Richter is Head of Data Science in Berlin with 12 years of experience bridging academic research and production ML, holding a PhD in statistics and a strong focus on AutoML and hyperparameter optimization. He’s a hands-on R engineer and core contributor to the widely used mlr/mlr3 ecosystems, where he led refactors, added parallelization, custom resampling, and multi-measure performance evaluation. At Civey he progressed from Senior R Engineer to Head of Data Science, combining leadership with deep technical craftsmanship in model evaluation and scalable pipelines. Beyond work he tutors R, maintains popular ML libraries, and brings a maker’s curiosity and photography eye to problem solving—evidence of a practitioner who values both precision and creativity.
12 years of coding experience
5 years of employment as a software developer
Master of Science (M.Sc.), Statistik, Master of Science (M.Sc.), Statistik at TU Dortmund UniversityMaster of Science (M.Sc.), Master of Science (M.Sc.) at TU Dortmund
Github Skills (15)
machine-learning10
performance-monitor10
predictive-modeling10
r-package10
mlr10
multiclass-classification10
data-science10
classification10
metric10
r10
auto-tuning9
fine-tuning9
testing9
statistics9
performance-tuning9
Programming languages (11)
Github contributions (5)
mlr-org/mlr
Nov 2013 - Nov 2020
Machine Learning in R
Role in this project:
Data Scientist
Data ScientistContributions:464 commits, 117 PRs, 531 pushes in 7 years 1 month
Contributions summary:Jakob primarily worked on the machine learning library, `mlr-org/mlr`. They implemented functionality for evaluating the performance of models. Specifically, the user refactored the performance metric calculation process and enhanced the library to allow the use of multiple performance measures simultaneously. Additionally, they added functionality for handling and tuning the threshold parameter in classification problems and added a new measure for multiclass problems.
imbalance-correctionlearnersensemble-learningclassificationr-package
mlr-org/mlr3
Aug 2018 - Apr 2021
mlr3: Machine Learning in R - next generation
Role in this project:Back-end Developer
Back-end DeveloperContributions:3 reviews, 15 commits, 7 PRs in 2 years 8 months
Contributions summary:Jakob primarily focused on refactoring and improving the codebase, specifically renaming and updating references. They updated code to use the `paradox` package, replacing instances of `phng`. They also addressed documentation inconsistencies and fixed issues related to `BenchmarkResult` construction. Furthermore, the user contributed to the implementation of custom resampling methods and added parallelization features for testing.
r-packageregressionnext-generationdata-sciencemachine-learning
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