Jan Beitner
Director, Data & AI
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolJan Beitner is a Director of Data & AI with 11 years' experience bridging deep technical expertise and private equity value creation, currently leading data and AI at Inflexion in London. He combines a PhD-level physics background from Cambridge with hands-on data science and managerial experience from BCG/BCG GAMMA to deliver production forecasting, personalization and optimization systems. Known for turning academic research into tangible business impact, he has contributed to popular open-source time-series forecasting tools (e.g., pytorch-forecasting) improving DeepAR and NBeats implementations. Jan excels at building teams and tech stacks that scale across the investment lifecycle, hiring the right talent and making complex transformations concrete and measurable.
11 years of coding experience
7 years of employment as a software developer
Bachelor of Science (B.Sc.) Business Economics, Bachelor of Science (B.Sc.) Business Economics at FernUniversität in HagenDoctor of Philosophy (PhD) Physics, Doctor of Philosophy (PhD) Physics at University of CambridgeBachelor of Science (B.Sc.) Physics, Bachelor of Science (B.Sc.) Physics at Ludwig-Maximilians-Universität MünchenBachelor Thesis (physics) Physics, Bachelor Thesis (physics) Physics at EPFL
German, English
Stackoverflow
1reputation
0reached
1answer
0questions
Github Skills (12)
neural-network10
forecasting10
pytorch10
machine-learning10
forecast10
deep-learning10
time-series-forecasting10
python10
pytorch-lightning8
pandas7
virtual-machine6
linux6
Programming languages (7)
Github contributions (5)
sktime/pytorch-forecasting
Aug 2020 - Oct 2022
Time series forecasting with PyTorch
Role in this project:
Data Scientist
Data ScientistContributions:30 releases, 73 reviews, 389 commits in 2 years 1 month
Contributions summary:Jan's commits primarily focus on modifications within the `pytorch-forecasting` repository, suggesting a role in enhancing time series forecasting models. The changes include code adjustments related to the DeepAR model, ensuring accurate indexing of target variables. They also involve the merging of branches for improvements and bug fixes, including updates related to the NBeats model and the handling of multiple targets. These contributions highlight a focus on improving the performance and functionality of time series forecasting algorithms using PyTorch.
pytorchtime-series-forecastingforecastinggpuuncertainty
conda-forge/pytorch-forecasting-feedstock
Sep 2020 - Sep 2024
A conda-smithy repository for pytorch-forecasting.
Contributions:1 review, 11 PRs, 8 pushes in 4 years
condapytorch
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