Marc Becker
IT-Business Analyst at REWE digital
Cologne, North Rhine-Westphalia, Germany
Summary
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Rockstar🎓
Top SchoolMarc Becker is an IT-Business Analyst based in Cologne with 8 years of experience at the intersection of business, engineering, and data-driven software. With a background in Wirtschaftsingenieurwesen (MSc) and practical roots in industrial research and operational excellence, he translates complex technical requirements into pragmatic solutions for product and process improvement. He currently works at REWE digital and combines hands-on web development training (React, Node, NoSQL) with analytical rigor from earlier roles at Fraunhofer and TU Dortmund. An active contributor to the mlr3 machine learning framework in R, he improved hotstarting behavior and added robust unit tests to enhance reproducibility and stability—showing attention to edge cases that often break production systems. Colleagues know him for bridging stakeholder needs with reliable technical implementations and for bringing manufacturing-informed systems thinking into digital product teams.
8 years of coding experience
Master of Science - MS, Wirtschaftsingenieurwesen, Master of Science - MS, Wirtschaftsingenieurwesen at Technische Universität Dortmund
Github Skills (7)
machine-learning10
r-package10
data-science10
r10
testing10
regression9
classification9
Programming languages (7)
Github contributions (5)
mlr-org/mlr3
Nov 2019 - Jan 2023
mlr3: Machine Learning in R - next generation
Role in this project:
Data Scientist
Data ScientistContributions:10 releases, 39 reviews, 146 commits in 3 years 3 months
Contributions summary:Marc primarily contributed to improving the functionality and robustness of the `mlr3` package, a machine learning framework in R. Their work focused on refining the interaction of learners within the framework, particularly around hotstarting techniques, and addressing edge cases in the code to prevent errors. The user added a series of unit tests to verify the proper function of hotstarting, ensuring reproducibility and enhancing the testing coverage of the package. They addressed issues related to parameter handling, data handling, and logging, improving the overall usability and stability of the library.
r-packageregressionnext-generationdata-sciencemachine-learning
r-universe/mlr-org
Sep 2020 - Feb 2023
Contributions:612 commits in 2 years 5 months
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