Bill Engels
Principal Data Scientist
Bend, Oregon, United States
Summary
🤩
Rockstar🎓
Top SchoolBill Engels is a Principal Data Scientist based in Bend, Oregon with 11 years of experience applying probabilistic modeling and statistical rigor to real-world problems across finance, energy, and startups. He holds an M.S. in Statistics and a physics B.S., and has moved between research-grade open-source work and production analytics—most notably contributing core Gaussian process features to the widely used PyMC probabilistic programming library. At PyMC Labs he blends back-end engineering with probabilistic modeling, building high-performance GP implementations and predictive-distribution utilities that leverage efficient linear algebra. His background includes applied roles at Ledger Investing, Portland General Electric, and Blockforce Capital, where he translated complex uncertainty into actionable decisions. Colleagues rely on him for thoughtful model design and clean, testable implementations that bridge research and production.
11 years of coding experience
5 years of employment as a software developer
Bachelor’s Degree Physics, Bachelor’s Degree Physics at University of OregonMasters Statistics, Masters Statistics at Portland State University
Github Skills (6)
mcmc10
develop10
python10
theano10
gaussian-processes10
linear-algebra9
Programming languages (6)
Github contributions (5)
pymc-devs/pymc
Nov 2016 - Oct 2022
Bayesian Modeling and Probabilistic Programming in Python
Role in this project:
Back-end Developer & Data Scientist
Back-end Developer & Data ScientistContributions:137 reviews, 249 commits, 77 PRs in 5 years 11 months
Contributions summary:Bill implemented and integrated core features of a Gaussian process framework for probabilistic programming in Python, specifically focusing on the implementation of new covariance functions like ExpQuad, RatQuad, and a locally periodic function, along with their tests. They rewrote the GP classes to support the addition of covariance functions and to leverage existing, higher-performance library functions such as cholesky decomposition. Additionally, they developed a utility for creating predictive distributions.
pythonbayesian-inferencestatistical-inferencemachine-learningprobabilistic-programming
bwengals/mplrcpub
May 2016 - Mar 2021
Contributions:7 pushes, 1 branch in 4 years 10 months
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