Thomas Neuer
Engagement Lead at Unit8
Zurich, Zurich, Switzerland
Summary
🤩
Rockstar🎓
Top SchoolThomas Neuer is an Engagement Lead at Unit8 with nine years of experience applying data engineering and data science to financial and pharmaceutical challenges, and four years focused on end-to-end delivery of production-ready data products. He combines a strong academic foundation in physics and biostatistics with hands-on roles across hospitals and consulting, bringing rigor from epidemiological research into practical cloud-based solutions. At Unit8 he progressed from data scientist to data engineer and now leads engagements, bridging technical, business, and domain experts to streamline operationalization. An active contributor to the popular darts time-series library, he has implemented forecasting features and model support (including VARIMA) that reflect deep expertise in time-series analysis and production concerns. Based in Zurich, he is particularly interested in cloud technologies and simplifying cross-team workflows to accelerate impact.
9 years of coding experience
7 years of employment as a software developer
Master of Science - MS, Physics, 5.9, Master of Science - MS, Physics, 5.9 at University of Zurich
Github Skills (11)
forecasting10
forecast10
time-series10
anomaly-detection10
python10
machine-learning9
pandas9
testing8
scikit7
scikit-learn7
numpy7
Programming languages (3)
Github contributions (5)
unit8co/darts
Feb 2021 - May 2021
A python library for user-friendly forecasting and anomaly detection on time series.
Role in this project:
Data Scientist
Data ScientistContributions:55 reviews, 28 commits, 19 PRs in 2 months
Contributions summary:Thomas primarily contributed to improving the `darts` library, which focuses on time series forecasting and anomaly detection. Their work involved fixing bugs related to the `gaps` method and implementing new features, such as the `diff` function, demonstrating a solid understanding of time series analysis. They added support for new models, like VARIMA, and developed the associated tests, highlighting their skills in applying forecasting techniques. Furthermore, they enhanced the library's functionality by including features for dummy indexing, which simplifies time series handling.
forecastingpython-libraryanomalypythontime-series-analysis
tneuer/Sorting
Oct 2018 - Feb 2021
Contributions:2 reviews, 9 PRs, 22 pushes in 2 years 4 months
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