Benedikt Heidrich
Data Scientist
Karlsruhe, Baden-Württemberg, Germany
Summary
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Rockstar🎓
Top SchoolBenedikt Heidrich is a data scientist with nine years of experience blending research-grade time series expertise and practical ML engineering, currently at Mercedes-Benz Tech Innovation. As a core developer of the widely used sktime library he has driven key features—adding foundation model support, a graphical pipeline, new estimators, and benchmark improvements—while fixing subtle data-handling bugs and expanding xarray compatibility. His background includes a PhD-track research role at KIT and hands-on software work across Fraunhofer and industry, giving him fluency in both academic rigor and production delivery. Known for turning advanced time-series methods into robust, usable tooling, he often bridges plotting/benchmarks and algorithmic implementations to make models easier to evaluate and deploy.
9 years of coding experience
Dr.-Ing., Informatics, Dr.-Ing., Informatics at Karlsruhe Institute of TechnologyMaster, Computer Science, 1,0, Master, Computer Science, 1,0 at Karlsruher Institut für Technologie (KIT)
English, German
Stackoverflow
214reputation
3kreached
13answers
4questions
Github Skills (20)
python10
data-science10
scikit10
pandas10
machine-learning10
time-series10
numpy10
forecasting10
scikit-learn10
forecast10
data-analysis10
scipy9
data-structures6
hibernate6
spring-boot6
Programming languages (7)
Github contributions (5)
sktime/sktime
Aug 2022 - Aug 2022
A unified framework for machine learning with time series
Role in this project:
Data Scientist
Data ScientistContributions:371 reviews, 2 commits, 123 PRs in 21 days
Contributions summary:Benedikt primarily contributed to bug fixes and enhancements within the `sktime` repository, focusing on time series analysis and machine learning tasks. They addressed issues related to data handling, specifically fixing bugs in the `check_equal_time_index` function and expanding support for `xarray` `DataArray` data types. The user's work included improvements to plotting utilities, such as fixing rendering issues and addressing deprecation warnings. Furthermore, the user introduced a `CurveFitForecaster` based on `scipy.optimize_curve` and made substantial improvements to the benchmarking module.
machine-learningtime-seriesscikit-learntime-series-classificationtime-series-regression
KIT-IAI/pywatts-pipeline
Jul 2022 - Feb 2023
pywatts-pipeline
Contributions:5 reviews, 79 commits, 37 PRs in 7 months
pythonpipelineworkflow
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