Jakob Richter
Head Of Data Science
Berlin, Germany
Summary
🤩
Rockstar🎓
Top SchoolJakob Richter is Head of Data Science at Civey in Berlin with a PhD in Statistics and 12 years of experience, known for translating academic machine learning into production-ready systems. He is a key contributor to the popular R ecosystems mlr and mlr3, where he refactored core code, added multiclass measures, threshold tuning, custom resampling, and parallel testing features. Jakob blends backend library design with product-focused analytics leadership, shepherding robust evaluation and benchmarking into real-world workflows. Outside of work he tutors R and pursues photography and maker projects, a practical-creativity mix that informs his detail-oriented approach to tooling and experimentation.
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
predictive-modeling10
r-package10
mlr10
multiclass-classification10
performancemonitor10
classification10
data-science10
metric10
r10
auto-tuning9
statistics9
fine-tuning9
testing9
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
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.