Maxim Fridental
Data Science And Data Engineering
Nuremberg, Bavaria, Germany
Summary
👤
Senior🎓
Top SchoolMaxim Fridental is a data science and data engineering leader with nine years of experience translating Big Data investments into measurable business value across product, UX and ML domains. Based in Nuremberg, he has built recommendation engines, fraud prevention systems and historical market-data platforms that serve millions of users and prevent losses at scale. His background blends hands-on Big Data engineering with product management and business administration, enabling him to align technical architectures with commercial outcomes. He has deep experience improving software quality, including test automation contributions to the Keras preprocessing ecosystem, and a pragmatic systems-engineering foundation from a Diplom in digital signal processing. Pragmatic and cross-disciplinary, he specializes in turning complex datasets into operational digital twins and actionable insights.
9 years of coding experience
21 years of employment as a software developer
Diplom Ingenieur, Digitale Signalverarbeitung, Hardware-nähe Software-Entwicklung, Mathe, Diplom Ingenieur, Digitale Signalverarbeitung, Hardware-nähe Software-Entwicklung, Mathe at South Ural State University
German, English, Russian, Chinese
Stackoverflow
33reputation
520reached
0answers
2questions
Github Skills (10)
pytest10
python10
numpy10
image-processing9
testing7
keras7
graph6
machine-learning6
classification6
autotools6
Programming languages (2)
Github contributions (5)
keras-team/keras-preprocessing
Aug 2018 - Sep 2018
Utilities for working with image data, text data, and sequence data.
Role in this project:
QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:10 commits, 3 PRs, 5 comments in 11 days
Contributions summary:Maxim primarily focused on improving the testing infrastructure and test cases for the Keras preprocessing library. They addressed issues related to image data generation, including ensuring stratified splitting and supporting the seed parameter. The user also fixed PEP8 style issues, improved test assertions, and ensured that tests correctly handled edge cases and potential errors. This work involved modifying and expanding the existing test suite to enhance the reliability and correctness of the library's image processing capabilities.
text-datasequencepythonimage-data
mfridental/iwlearn
May 2019 - Jul 2022
"Production First" Machine Learning Framework
Contributions:4 commits, 2 PRs, 4 pushes in 3 years 2 months
machine-learning
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