Marcus Voß
Responsible AI Lead & AI Engineer at Birds on Mars
Berlin, Germany
Summary
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Senior🎓
Top SchoolMarcus Voß is a Responsible AI lead and AI engineer based in Berlin with over a decade of experience bridging applied research and product delivery. He builds and operationalizes trustworthy AI solutions at Birds on Mars while pursuing a Dr.-Ing. at TU Berlin, combining hands-on engineering with rigorous academic methods. His background in energy systems, forecasting and optimization has driven research projects and product ownership roles that connect academia, industry partners and students. Marcus regularly teaches practical data science and time-series courses, and has supervised numerous theses, reflecting a talent for translating complex methods into usable tools. He is active in Data4Good initiatives (Climate Change AI, CorrelAid), signaling a commitment to socially impactful AI beyond commercial work. A detail that sets him apart is his dual fluency in research-grade modeling and production-focused intelligence architecture, enabling both prototype innovation and robust deployment.
10 years of coding experience
5 years of employment as a software developer
Dr.-Ing., Computer Science, Magna cum laude (1.0), Dr.-Ing., Computer Science, Magna cum laude (1.0) at Technische Universität BerlinMaster of Science (M.Sc.), Information Systems (Wirtschaftsinformatik), Master of Science (M.Sc.), Information Systems (Wirtschaftsinformatik) at Humboldt University of BerlinComputer Science / Economics, Computer Science / Economics at Københavns UniversitetBachelor of Science (B.Sc.), Information Systems (Wirtschaftsinformatik), Bachelor of Science (B.Sc.), Information Systems (Wirtschaftsinformatik) at The Berlin School of Economics and Law
Github Skills (44)
python10
statistics10
data-science9
scikit9
machine-learning9
scikit-learn9
data-analysis9
portable7
xgboost7
java7
gradient7
edit-distance7
climate-change6
scalable6
distributed-systems6
Programming languages (5)
Github contributions (5)
low-voltage-loadforecasting/low-voltage-loadforecasting.github.io
Jun 2021 - Sep 2022
This is the repository for the Github pages site produced with Jekyll to our page hosting an overview of load forecasting data sets as presented in our preprint on arXiv.
Contributions:88 commits, 1 PR, 96 pushes in 1 year 3 months
CorrelAid/correlaid-matplotlib-style
Dec 2020 - Jan 2021
Contributions:1 release, 33 commits, 28 pushes in 29 days
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