Thomas Aarholt is a Senior Data Scientist and manager with a decade of experience turning academic-grade microscopy, machine learning and statistical methods into production forecasting and logistics optimizations. He holds a DPhil from Oxford where he co-developed the widely used hyperspy package and applied dimensionality reduction and curve fitting to study nuclear-material failure mechanisms related to Fukushima. At Oda he built demand-forecasting pipelines, led practice improvements across 20+ data scientists, and cut forklift travel distances by over 50% using polars, georeferencing and custom optimization simulations. A pragmatic engineer, he contributes to major open-source projects (SymPy, PyMC, Polars, Matplotlib) including GPU support and stability enhancements, bridging research-grade tools and production systems. Now at Microsoft, he pairs deep scientific rigor with product-minded delivery, skilled at turning noisy experimental and operational data into robust, deployable solutions. An unconventional mix of dancer, open-source maintainer and former electron-microscopy researcher, he thrives where hard science meets scalable engineering.
10 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy, Nuclear Materials, Doctor of Philosophy, Nuclear Materials at University of Oxford
Bachelor of Science, Nanotechnology, First Honours, Bachelor of Science, Nanotechnology, First Honours at University of Leeds
International Baccalaurate Diploma, Higher Level: Physics, Chemistry, Maths and English. Standard Level: Economics and Norwegian., International Baccalaurate Diploma, Higher Level: Physics, Chemistry, Maths and English. Standard Level: Economics and Norwegian. at Lillestrøm High School
Contributions:82 reviews, 208 commits, 106 PRs in 6 years
Contributions summary:Thomas contributed to the project by implementing and improving key functionalities within the codebase. Their work included adding external links, fixing image links, and adding methods to the BaseSignal and EELS classes. Furthermore, the user made several changes to the code related to linear binning, specifically addressing the order of the binning factors in signal dimensions. They also updated dependencies for the project.
Bayesian Modeling and Probabilistic Programming in Python
Role in this project:
Back-end Developer
Contributions:14 reviews, 2 commits, 8 PRs in 1 day
Contributions summary:Thomas primarily focused on improving the PyMC library's functionality and maintainability. Their contributions included fixing warnings, enhancing type hinting within the sampling module, and preventing unbound traces due to type hints. Additionally, they removed deprecated distribution keywords and simplified the model's metaclass and new method. These changes likely improve the library's stability, usability, and code quality for Bayesian modeling and probabilistic programming tasks.
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.