Jörg Dietrich
Head Of AI For R&D at AUMOVIO
Mainz, Rhineland-Palatinate, Germany
Summary
👤
Senior🎓
Top SchoolJörg Dietrich is Head of AI for R&D with 14 years of experience uniting advanced data science, physics-rooted analytical rigor, and product-driven AI delivery to move projects from research to production. He builds human-centered AI that amplifies expert decision-making by automating repetitive work, and has led diverse, high-performance teams across global R&D organizations including Continental and AUMOVIO. His scientific background (Dr. rer. nat. in Astronomy) and long research tenure at institutions like LMU Munich and ESO give him a rare ability to separate hype from real value and to apply Bayesian and probabilistic methods where they matter. A hands-on contributor to scientific open-source projects—adding statistical tests to SciPy and improving Dask, Astropy and Pelican plugins—he blends low-level algorithmic work with scalable engineering. Colleagues rely on him for turning complex multivariate models and high-performance code into usable products that deliver measurable business impact.
14 years of coding experience
11 years of employment as a software developer
Dr. rer. nat., Astronomie, magna cum laude, Dr. rer. nat., Astronomie, magna cum laude at Rheinische Friedrich-Wilhelms-Universität BonnPhysics, Physics at University of Tennessee, Knoxville
German, English
Github Skills (25)
algorithms10
scipy10
javascript10
cosmology10
python10
jinja210
astronomy10
dataframes10
pandas10
astrophysics10
dataframe10
numpy10
astropy10
pelican10
dask10
Programming languages (9)
Github contributions (5)
dask/dask
Mar 2018 - Feb 2020
Parallel computing with task scheduling
Role in this project:
Back-end Developer
Back-end DeveloperContributions:6 PRs, 12 comments, 4 issues in 1 year 11 months
Contributions summary:Jörg primarily contributes to the Dask project by addressing various issues related to the DataFrame module. Their work includes fixing typos, enhancing HDF format compatibility with Pandas, removing outdated documentation requirements, optimizing the aggregation logic, and supporting pathlib paths in read_hdf. These changes indicate a focus on improving the functionality, usability, and integration of Dask's DataFrame features.
pythonschedulingparallelnumpydask
astropy/astropy
Dec 2013 - Apr 2017
Astronomy and astrophysics core library
Role in this project:Back-end Developer
Back-end DeveloperContributions:24 commits, 9 PRs, 30 comments in 3 years 4 months
Contributions summary:Jörg primarily contributed to the `astropy/astropy` repository by modifying and extending cosmology-related classes. These changes include adding new parameters like baryonic and dark matter density and their evolution, as well as updating methods related to distance calculations. The contributions also involved documentation updates and typo fixes, which enhance the usability and accuracy of the core astronomy library. The modifications improve the accuracy of the library related to cosmological calculations.
astrologypythonscienceastrophysicsastrodynamics
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