Peter Tillmann
Machine Learning Ingenieur at SABO Mobile IT GmbH
Freiburg im Breisgau, Baden-Württemberg, Germany
Summary
👤
Senior🎓
Top SchoolPeter Tillmann is a Machine Learning Engineer with nine years of experience applying ML, forecasting and optimization to energy systems, manufacturing and dynamic pricing. He builds production-ready forecasting pipelines and ML models—most recently delivering day-ahead low-voltage grid load forecasts and integrations that enable proactive grid control. His background spans PhD-level photovoltaics modelling (bifacial and tandem cells) and hands-on data engineering for gigabyte-scale time series, combining physics-based simulation, Bayesian optimisation and modern ML stacks (PyTorch, XGBoost, SQL/cloud workflows). Peter has moved research tools into industry use—creating open-source simulation code during his PhD and an LLM-powered natural-language SQL assistant in production at Solarlab—showing a knack for turning complex science into usable software. Based in Freiburg, he thrives in interdisciplinary teams at the intersection of energy expertise, software engineering and data-driven decision making.
9 years of coding experience
3 years of employment as a software developer
Master, Chemie, Master, Chemie at Freie Universität Berlin
Stackoverflow
2,110reputation
153kreached
53answers
1question
pandas
top-5%
python
top-5%
Github Skills (71)
python10
pandas10
data-science10
data-analysis10
manipulation10
bioinformatics10
frame10
dataframes9
plotly9
numba9
sympy9
parallel9
charting9
data-manipulation9
no-javascript9
Programming languages (3)
Github contributions (5)
P-Tillmann/pandas
Jul 2017 - Jun 2021
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Contributions:2 PRs, 24 pushes, 2 branches in 4 years
polarspythondatalabeled-datamanipulation
nano-sippe/bifacial_illumination
Apr 2019 - Nov 2022
Contributions:1 release, 7 commits, 17 pushes in 3 years 6 months
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