Philip Anderson
Staff Data Scientist
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolPhilip Anderson is a Staff Data Scientist in San Francisco with a decade of experience translating statistics and causal machine learning into business impact across high-scale consumer products. He led membership growth science at Uber—designing A/B tests and causal models that now inform over $500M in annual investments—and recently joined Meta to continue applying experimentation and production ML at scale. His background spans recommendation systems, Bayesian test measurement, and building Tier-1 data assets and serving hundreds of millions of predictions in production. Philip pairs rigorous academic training (Notre Dame BA, Texas A&M M.S. in Statistics) with hands-on engineering skills in Python, PySpark, and data platform design. He’s bridged analytics and product for Fortune 50 and platform companies, often acting as the science liaison to engineering and senior leadership. Outside core work he mentors, interviews extensively, and has a long-standing habit of turning internal research code into reusable tools and data assets.
10 years of coding experience
10 years of employment as a software developer
BA Economics, BA Economics at University of Notre Dame, London ProgramMaster of Science (M.S.) Statistics, Master of Science (M.S.) Statistics at Texas A&M UniversityBA Economics, BA Economics at University of Notre Dame
English
Github Skills (3)
react4
nextjs3
typescript3
Github contributions (5)
panders225/r_univariate
Jan 2016 - Apr 2017
Contributions:11 commits, 11 pushes, 2 branches in 1 year 3 months
panders225/bayesian_methods
Sep 2016 - Apr 2017
Contributions:47 pushes, 1 branch in 6 months
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