Charles Bradshaw
Senior Software Engineer at Meta
Menlo Park, California, United States
Summary
🤩
Rockstar🎓
Top SchoolCharles Bradshaw is a senior software engineer with 10 years of experience building user-focused tools that take teams from data preparation to deploying profitable ML models. Based in Menlo Park, he blends hands-on engineering at Meta and Google with entrepreneurial leadership as CEO of Abyss Investments, consistently shipping automation and APIs that reduce the friction in productionizing machine learning. An active open-source contributor, he helped implement advanced feature engineering (including cumulative primitives and cutoff-time support) for the widely used Featuretools library. He pairs a mathematical curiosity—evident in his interest in quadcopters and theoretical exploration—with practical systems design, ensuring models are not just built but reliably deployed. His background spans startups and hyperscalers, giving him a knack for translating research-grade ideas into customer-ready products.
10 years of coding experience
7 years of employment as a software developer
Bachelor of Science (B.S.) Computer Science, Bachelor of Science (B.S.) Computer Science at Virginia Commonwealth University
Stackoverflow
1reputation
0reached
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Github Skills (10)
scikit10
testing10
pandas10
machine-learning10
pytest10
python10
scikit-learn10
automl9
numpy9
automated-machine-learning9
Programming languages (2)
Github contributions (5)
alteryx/featuretools
Oct 2017 - May 2019
An open source python library for automated feature engineering
Role in this project:
Data Scientist
Data ScientistContributions:10 commits, 18 PRs, 56 pushes in 1 year 6 months
Contributions summary:Charles primarily contributed to the implementation of feature engineering functionalities within the `featuretools` library. Their work involved adding new features, modifying existing feature calculation logic, and incorporating cutoff time support for more accurate feature calculations. The user also refactored code, added tests, and updated documentation, enhancing the usability and reliability of the library. Further contributions include the addition of cumulative primitives and a new configuration object.
python-librarypythondata-scienceautomated-machine-learningmachine-learning
jvlians/operating-system-final-project
Nov 2016 - Dec 2016
Contributions:33 pushes in 10 days
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