Jonathan Wood
Software Engineering Lead at LexisNexis
Fuquay-Varina, North Carolina, United States
Summary
🤩
Rockstar🎓
Top SchoolJonathan Wood is a Software Engineering Lead with over 15 years of experience in the C#/.NET ecosystem, currently guiding teams at LexisNexis from Fuquay-Varina, NC. He blends hands-on engineering—spanning ML.NET contributions and data science consulting—with a people-first leadership style focused on mentoring, continuous learning, and delivery quality. His background includes applied data science work with Python, R, Azure ML, and Power BI, and he has contributed to notable open-source projects in ML.NET, improving both models and API documentation for FastTree. Jonathan’s early work in government and consulting honed a pragmatic approach to reliability and payment-processing accuracy under high-volume constraints. Colleagues know him as a coach who pairs technical depth in C# and machine learning with clear communication and process improvements. He often surfaces small but impactful usability fixes—like clearer docs and error messages—that make libraries easier for other engineers to adopt.
13 years of coding experience
11 years of employment as a software developer
Bachelor's degree Mathematics and Computer Science, Bachelor's degree Mathematics and Computer Science at University of South Carolina Aiken
Stackoverflow
2,704reputation
146kreached
82answers
23questions
azure
top-5%
Github Skills (22)
unit-testing10
algorithms10
mldotnet10
net10
nunit10
dotnet10
machine-learning10
tracks10
asp-net10
mlnet10
dotnet-core10
csharp10
test-automation10
ranking9
automl9
Programming languages (17)
Github contributions (5)
dotnet/machinelearning
Jun 2018 - Oct 2021
ML.NET is an open source and cross-platform machine learning framework for .NET.
Role in this project:
ML Engineer
ML EngineerContributions:1 review, 28 commits, 38 PRs in 3 years 4 months
Contributions summary:Jonathan primarily focused on updating and refining the help text and API documentation related to the FastTree algorithm within the ML.NET framework. They addressed wording inconsistencies and improved the clarity of argument descriptions. Additionally, the user corrected JSON formatting and updated error messages to point to GitHub issues. These contributions show a focus on improving the usability and maintainability of the ML.NET library, particularly for the FastTree module.
dotnetmachine-learningml
dotnet/machinelearning-samples
Jun 2018 - Dec 2021
Samples for ML.NET, an open source and cross-platform machine learning framework for .NET.
Role in this project:ML Engineer
ML EngineerContributions:16 reviews, 7 commits, 12 PRs in 3 years 6 months
Contributions summary:Jonathan primarily contributed to machine learning model development and related tasks within the ML.NET framework. They modified existing code to incorporate new features, updated data for sentiment analysis and added AutoML ranking samples. Their work included code changes related to the use of data loaders, pipeline configurations, evaluation metrics, model refitting, and prediction engines.
dotnetmachine-learningcsharpml
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