Daniel Wooten
Principal Associate, Data Science
Berkeley, California, United States
Summary
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Senior🎓
Top SchoolDaniel Wooten is a Principal Associate in Data Science based in Berkeley with 11 years of experience applying systems-level software engineering and advanced ML to high-stakes domains. He blends deep C/C++ and Python/R systems coding, parallel programming (MPI/OpenMP), and production ML—recently building and deploying reinforcement-learning recommender systems and risk-sloping deep models for AML at Capital One. His background in nuclear engineering research and Monte Carlo simulation informs a rigorous, simulation-first approach to algorithm design and computational best practices. He’s shipped human-aware control for autonomous swarms and end-to-end ML pipelines on AWS, demonstrating both model research and reliable operational deployment. Notably, his work couples multi-objective optimization and operator-in-the-loop simulations—bringing scientific rigor to real-world decision systems.
11 years of coding experience
5 years of employment as a software developer
Bachelor of Science (B.S.), Nuclear Engineering, Suma Cum Laude, 3.932, Bachelor of Science (B.S.), Nuclear Engineering, Suma Cum Laude, 3.932 at North Carolina State UniversityNuclear Engineering, Nuclear Engineering at UC Berkeley
EnglishProgramming languages (1)
Github contributions (5)
ddwooten/website
Jun 2020 - Aug 2024
Contributions:142 pushes in 4 years 3 months
ddwooten/Research_Codes
Jan 2015 - Jun 2019
Contributions:228 pushes, 1 branch in 4 years 5 months
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