Gabriel Stechschulte is a Senior Research Associate based in Lucerne with six years of experience applying probabilistic programming, optimization, and ML to engineering and IoT time-series problems. He has moved from hands-on research and data mining to engineering roles where he built database systems for time-series data, data pipelines with dbt, and models for manufacturing and energy optimization. Gabriel contributed to Google Summer of Code 2023 under NumFOCUS to improve interpretability tooling for Bambi probabilistic regressions, reflecting a focus on making complex Bayesian models actionable. His background in economics and applied information & data science gives him a rare combination of statistical rigor and business-oriented modeling. Notably, he designed the initial architecture for SANElib, a library to generate ML-ready SQL, showing an interest in bridging SQL-first data workflows with machine learning. He excels at turning messy IoT and operational data into interpretable, deployable models that inform process decisions.
6 years of coding experience
5 years of employment as a software developer
Bachelor of Science - BS Economics, Bachelor of Science - BS Economics at Bowling Green State University
Exchange Program Finance and Economics, Exchange Program Finance and Economics at Deakin University
Master of Science - MS Applied Information and Data Science, Master of Science - MS Applied Information and Data Science at Lucerne University of Applied Sciences and Arts
BAyesian Model-Building Interface (Bambi) in Python.
Contributions:2 PRs, 162 pushes, 63 branches in 1 year 9 months
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