Matthew Agee is a Machine Learning Engineer with a decade of experience delivering data-driven solutions across academia and industry. He blends applied mathematics and chemical physics with hands-on ML and software engineering, currently building production data pipelines and NLP tools at The Clorox Company. His background includes optimizing scientific software to deliver as much as 40x performance gains and automating workflows that reduce setup times from hours to minutes, reflecting a knack for turning complex problems into practical tooling. At Clorox he sets up data pipelines with Apache Airflow and Google Cloud Dataflow and productionizes DS applications using Django and React on Google App Engine, while also building tools to inspect consumer clusters and analyze text responses. He began in computational chemistry and pivoted to data science and scalable software, pursuing roles that blend math, modeling, and software to extract actionable insights. Based in San Jose, CA, he holds a B.S. in Applied Mathematics and Chemical Physics from UC San Diego and an M.S. in Theoretical Chemistry from UC Irvine, demonstrating a strong academic foundation and a production-focused mindset.
10 years of coding experience
4 years of employment as a software developer
University of California, San Diego
Master’s Degree, Theoretical Chemistry, Master’s Degree, Theoretical Chemistry at University of California, Irvine
Contributions:2 PRs, 22 pushes, 1 branch in 7 months
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