DJ Rich
Machine Learning Engineer And Founder at True Theta
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolDJ Rich is a Machine Learning Engineer and founder with over a decade of experience building and deploying ML systems that drive pricing, recommendation, and decision automation. After five years as a Senior Research Scientist at Lyft where he helped design causal forecasting and pricing infrastructure used across the business, he founded True Theta to help companies build performant, reliable, and profitable ML products. His background spans fintech and quant trading—having built loan-purchasing models and portfolio risk systems—and credit and fraud modeling at LendUp, giving him deep experience with high-stakes predictive systems. DJ is also an active contributor to probabilistic ML tooling on GitHub, translating academic MATLAB examples into Python visualizations for Kevin Murphy’s influential text, signaling a strong blend of research rigor and production taste. Based in San Francisco, he pairs a UC Berkeley Financial Engineering master’s with hands-on startup and hedge-fund experience to deliver pragmatic, causal-driven AI solutions.
9 years of coding experience
9 years of employment as a software developer
Master's Degree, Financial Engineering, Master's Degree, Financial Engineering at University of California, BerkeleyBachelor's Degree, Mathematics and Economics, Bachelor's Degree, Mathematics and Economics at Boston College
Github Skills (19)
data-visualizations10
bayesian-statistics10
scipy10
python10
matplotlib10
data-modeling10
machine-learning10
data-visualisation10
probabilistic-reasoning10
numpy10
linear-regression10
bayesian10
statistical-models10
probabilistic-models10
data-visualization10
Programming languages (2)
Github contributions (5)
probml/pyprobml
Sep 2016 - Sep 2019
Python code for "Probabilistic Machine learning" book by Kevin Murphy
Role in this project:
Data Scientist
Data ScientistContributions:28 commits, 29 PRs, 1 comment in 3 years
Contributions summary:DJ contributed Python scripts to create visualizations and demos related to probabilistic machine learning concepts. They developed visualizations for binomial distributions, Gaussian probability density functions, and the Dirichlet distribution. The user also implemented demonstrations for linear regression, including model selection, posterior predictive distributions, and Bayesian linear regression, utilizing libraries such as NumPy, Matplotlib, and SciPy. Furthermore, they rewrote and adapted code from existing MATLAB files to Python, demonstrating a focus on reproducing figures and examples from the "Probabilistic Machine Learning" book.
pythonpmlflaxcolabpymc3
Duane321/pyprobml
Sep 2016 - Sep 2019
Python code for "Machine learning: a probabilistic perspective"
Contributions:2 PRs, 34 pushes, 1 branch in 3 years
python-codefairness-mlpythonperspectivedeep-learning
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