Joseph Higgins
Machine Learning Engineer at Meta
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolJoseph Higgins is a Machine Learning Engineer with nine years of experience applying statistics and applied mathematics to high-impact problems across tech, consulting, and national labs. He has led data-driven projects at Meta and Lyft—spanning generative AI, mapping/localization, and marketplace incentives—and previously researched active learning for NLP at Lawrence Livermore. Comfortable moving between product, research, and operations, he blends time-series forecasting and statistical rigor with practical deployment experience from Facebook and Oliver Wyman. Based in San Francisco and trained at Stanford (MS Statistics & Computational Engineering) and Harvard (AB Applied Mathematics), he now channels his domain expertise into improving competitive esports through rigorous analytics. An unconventional thread through his career is translating consulting-born problem decomposition into reproducible ML solutions that scale in production environments.
9 years of coding experience
8 years of employment as a software developer
A.B. Applied Mathematics (Geophysics), A.B. Applied Mathematics (Geophysics) at Harvard UniversityMaster of Science - MS Statistics & Computational Engineering, Master of Science - MS Statistics & Computational Engineering at Stanford UniversityGithub contributions (5)
joehiggins/cme213
Apr 2018 - Jun 2018
Contributions:121 pushes, 1 branch in 2 months
joehiggins/cme307
Jan 2018 - Mar 2018
Contributions:38 commits, 32 pushes, 1 branch in 1 month
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