Peter Foley
Head Of Data Science
Pasadena, California, United States
Summary
👤
Senior🎓
Top SchoolPeter Foley is Head of Data Science with a decade of experience building and leading teams that turn causal inference and machine learning into production products for media and advertising. He currently architects the modeling core for EDO’s AdEngage, predicting household-level responses to TV and streaming ad exposures while maintaining clean, maintainable code and deployment pipelines. A former founder and analytics lead in political and media spaces, he combines deep expertise in experiment design, heterogeneous treatment effect analysis, and behavioral modeling with hands-on ML engineering. Peter is an active contributor to open-source causal ML (notably refactoring and validating components in uber/causalml) and brings a rare blend of academic rigour from Caltech and practical product-driven delivery.
10 years of coding experience
9 years of employment as a software developer
California Institute of Technology
English, French, Spanish
Github Skills (10)
scikit10
lift10
xgboost10
machine-learning10
lifting10
python10
modeling10
causal-inference10
scikit-learn10
data-analysis8
Programming languages (7)
Github contributions (5)
uber/causalml
Dec 2019 - Jan 2020
Uplift modeling and causal inference with machine learning algorithms
Role in this project:
ML Engineer
ML EngineerContributions:7 commits, 5 PRs, 19 comments in 28 days
Contributions summary:Peter contributed significantly to the `causalml` repository, focusing on improvements and refactoring of existing causal inference and uplift modeling tools. Their commits include fixing typos, generalizing existing functions, and adding treatment validation across various learner classes. They also refactored and validated the `CausalTreeRegressor` and made updates related to the XGBoost library.
fairness-mldeep-learningmachine-learning-algorithmscausalinference
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