Matthew Agee
Machine Learning Engineer at The Clorox Company
San Jose, California, United States
Summary
👤
Senior🎓
Top SchoolMatthew Agee is a machine learning engineer with 11 years of experience blending applied mathematics, chemical physics, and production ML to turn research-grade algorithms into reliable data products. Based in San Jose, he has built and maintained cloud-native data pipelines and deployed ML-driven web applications at The Clorox Company using Airflow, Dataflow, Django, React, and Google App Engine. His graduate work demonstrates deep algorithmic chops—deriving faster equations and implementing Fortran and Python solutions that reduced runtimes by orders of magnitude and automated complex scientific workflows. He brings practical NLP and embedding experience from product-review scraping and topic modeling to consumer insights tools, plus hands-on optimization of legacy scientific codebases. Not obviously visible from his title, Matthew’s background in chemical physics gives him a knack for extracting signal from high-dimensional scientific data and turning it into production-ready analytics. He’s focused on applying rigorous math and engineering to scalable data science problems that impact product and consumer understanding.
10 years of coding experience
4 years of employment as a software developer
University of California, San DiegoUniversity of California, Irvine
English, Spanish
Github Skills (29)
python10
jupyter10
image-processing10
image10
computer-vision10
bokeh9
javascript9
visualization9
holoviews9
machine-learning9
data-visualisation9
browser9
altair9
deep-learning9
ipywidgets9
Programming languages (5)
Github contributions (5)
magee256/yolo_v2
Jan 2018 - Aug 2018
Contributions:20 commits, 5 pushes, 1 branch in 7 months
magee256/TurbomoleScripts
Sep 2016 - Apr 2017
Contributions:2 PRs, 22 pushes, 1 branch in 7 months
calculationmechanicalquantumquantum-computing
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