Rogan Carr
Managing Director at Moody's
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolRogan Carr is a seasoned machine learning and software engineering leader with nine years of industry experience and a PhD in Physics, now serving as Managing Director at Moody’s in Seattle. He blends deep research pedigree—from postdoctoral work in metagenomics and computational physics—to product-focused applied science roles at Microsoft, where he led ML toolkits and contextual inference platforms. Rogan has a strong track record shipping interpretable ML and scalable algorithms, contributing to prominent open-source projects like ML.NET (notably on GAMs and FastTree ranking) and building core data science workflows for production teams. Known for turning complex analytical problems into robust, documented solutions, he also brings hands-on systems work across C#, Java, and distributed processing. Pragmatic and curious, he pairs academic rigor with a history of mentoring cross-functional teams and shipping reproducible, explainable ML in production.
9 years of coding experience
21 years of employment as a software developer
Bachelor of Science (B.S.), Physics, Bachelor of Science (B.S.), Physics at University of WashingtonChemistry, Chemistry at New York UniversityUniversity of Illinois Urbana-Champaign
Github Skills (9)
net10
asp-net10
algorithms10
machine-learning10
dotnet10
mlnet10
csharp10
mldotnet10
dotnet-core10
Programming languages (4)
Github contributions (5)
dotnet/machinelearning
Jun 2018 - Apr 2019
ML.NET is an open source and cross-platform machine learning framework for .NET.
Role in this project:
ML Engineer
ML EngineerContributions:62 commits, 117 PRs, 56 pushes in 10 months
Contributions summary:Rogan primarily contributed to the ML.NET machine learning framework by addressing issues and implementing features related to generalized additive models (GAMs) and the FastTree ranking algorithm. Their work includes fixing bugs related to loss metrics and the MaxDCG property in FastTreeRanking, and expanding the GAM models to include methods that return model parameters. The user also updated documentation, adding detailed explanations for Permutation Feature Importance and GAMs, and created samples demonstrating the use of GAMs and Permutation Feature Importance. Additionally, they refactored components related to GAM predictors and added tests to validate the API.
dotnetmachine-learningml
rogancarr/machinelearning
May 2018 - Apr 2019
ML.NET is an open source and cross-platform machine learning framework for .NET.
Contributions:326 pushes, 134 branches in 10 months
dotnetmachine-learning
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