Daniel Schmitz - Cloud Data Engineer

Daniel Schmitz

Cloud Data Engineer

Germany

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Summary

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Rockstar

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Top School

Daniel Schmitz is a Cloud Data Engineer with seven years of experience combining academic rigor from a PhD in physics with practical expertise in regression analysis, Bayesian statistics, and machine learning using Python. He has moved between research and industry roles—from postdoctoral research and consulting to cloud-focused engineering—bringing a strong foundation in numerical methods to production data systems. As a SciPy maintainer and contributor, he has improved core optimization algorithms (dual_annealing, shgo) and strengthened tests, demonstrating attention to algorithmic accuracy that benefits scientific and engineering users. Based in Germany, he blends deep statistical modeling skills with cloud engineering to operationalize complex analytics at scale, and his background in medical and neuroimaging research gives him a unique perspective on applying ML to real-world, noisy data.

8 years of coding experience

3 years of employment as a software developer

PhD, Physics, PhD, Physics at Bergische Universität Wuppertal

Bachelor of Science (B.Sc.), Physik, Bachelor of Science (B.Sc.), Physik at Universität Siegen

Master of Science (M.Sc.), Medical Physics, Master of Science (M.Sc.), Medical Physics at Heinrich-Heine-Universität Düsseldorf

German, English, French

Github Skills (10)

algorithm10

algorithms10

scipy10

python10

optimizers10

optimisation10

scientific-computing10

optimization10

numpy10

testing10

Programming languages (8)

TypeScriptJavaC++RCPythonCudaFortran

Github contributions (5)

scipy/scipy

Jan 2022 - Jan 2023

SciPy library main repository

Role in this project:

Back-end Developer & Algorithm Implementer

Contributions:516 reviews, 50 commits, 214 PRs in 1 year

Contributions summary:Daniel primarily contributed to the optimization and extension of existing algorithms within the SciPy library. They improved the functionality of the `dual_annealing` and `shgo` optimization algorithms by adding support for the `Bounds` class. Furthermore, the user made changes to tests, including strengthening existing tests for shgo and improving the accuracy of the vonmises entropy implementation. Their contributions focused on enhancing the accuracy and usability of the optimization algorithms within the SciPy library.

scipypythonscientific-computing

dschmitz89/scipybiteopt

Oct 2021 - Jul 2023

A python wrapper for the global optimizer biteopt

Contributions:4 releases, 3 PRs, 43 pushes in 1 year 9 months

pythonnonlinearevolutionary-algorithmspython-wrapperoptimization

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