John Wittenauer
Advanced Senior Data Scientist
Findlay, Ohio, United States
Summary
🤩
Rockstar🎓
Top SchoolJohn Wittenauer is an advanced senior data scientist and AI engineer with over a decade of hands-on experience delivering production ML solutions at Marathon Petroleum, rising from IT systems integrator to staff-level data science leadership. He blends deep practical software engineering—evidenced by contributions like a scikit-learn wrapper for Keras and a public IPython notebook collection—with applied industrial analytics and model deployment at scale. Holding a M.S. in Computer Science and a B.S. in Computer Science & Engineering, he thrives at the intersection of data, software, and operations in enterprise environments. An author, investor, and entrepreneur by interest, he pairs rigorous technical craft with a curiosity for teaching fundamentals, reflected in his educational notebooks and test-driven contributions to open-source ML tooling.
13 years of coding experience
5 years of employment as a software developer
M.S. Computer Science, M.S. Computer Science at Bowling Green State UniversityHubbard High SchoolB.S. Computer Science & Engineering, B.S. Computer Science & Engineering at The Ohio State University
Github Skills (18)
integrate10
python10
scikit10
machine-learning10
integrations10
keras10
scikit-learn10
neural-network10
data-integration10
data-science9
testing9
notebook9
ipython9
jupyter-notebook9
data-structures8
Programming languages (3)
Github contributions (5)
jdwittenauer/ipython-notebooks
May 2014 - Mar 2019
A collection of IPython notebooks covering various topics.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:141 commits, 5 PRs, 86 pushes in 4 years 10 months
Contributions summary:John appears to be focused on developing the intro IPython notebook, saving progress on the notebook repeatedly. Based on the code differences, the user is working through examples of Python syntax and basic data structures, suggesting an effort to understand the fundamentals of Python.
pythonnumpyjupyter-notebookipython-notebookscovering
keras-team/keras
May 2015 - Aug 2015
Deep Learning for humans
Role in this project:ML Engineer
ML EngineerContributions:8 commits, 2 PRs, 7 comments in 2 months
Contributions summary:John contributed to the development of a scikit-learn wrapper for Keras models, enabling their integration with the scikit-learn ecosystem. They added functionality for classification and regression tasks, incorporating features like model compilation, fitting, prediction, and scoring. Furthermore, the user implemented tests to validate the wrapper's functionality and compatibility with Keras models. This demonstrates a focus on providing a bridge between two popular machine learning libraries.
pythondata-sciencedeep-learningneural-networksmachine-learning
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