Michael Chestnut
Senior Software Engineer Technical Lead at product impact
Dallas, Texas, United States
Summary
👤
Senior🎓
Top SchoolMichael Chestnut is a Senior Software Engineer and Technical Lead based in Dallas with 7 years of experience building backend systems, distributed data pipelines, and cloud-native infrastructure using Python and Go. He has driven revenue-impacting features—like virtual asset simulation and a recommendation engine at Stem—and architected high-throughput MLOps pipelines that scaled processing by ~100x while halving latency. Michael blends hands-on coding with infrastructure automation (Terraform, GitHub Actions, AWS) and team leadership, consistently translating product priorities into delivered systems. An active open-source contributor to Yellowbrick, he has improved ML visualization tooling and testing for model diagnostics, reflecting a practical focus on developer experience for ML workflows. His background in psychology and business, plus a data science certificate from Georgetown, gives him a rare mix of technical, analytical, and user-centered perspective.
7 years of coding experience
6 years of employment as a software developer
Certificate, Data Science, Completed, Certificate, Data Science, Completed at Georgetown UniversityBachelor’s Degree, Psychology, Business, Bachelor’s Degree, Psychology, Business at George Mason University
Github Skills (8)
scikit-learn10
machine-learning10
visualization10
visualizations10
python10
matplotlib10
scikit10
documentation8
Programming languages (5)
Github contributions (5)
DistrictDataLabs/yellowbrick
Oct 2019 - Feb 2020
Visual analysis and diagnostic tools to facilitate machine learning model selection.
Role in this project:
ML Engineer
ML EngineerContributions:5 commits, 5 PRs, 8 comments in 3 months
Contributions summary:Michael primarily contributed to the development and enhancement of machine learning visualization tools within the yellowbrick library. Their work included implementing "quick methods" for existing visualizers, such as ROCAUC and TSNE, enabling faster and more accessible usage. The commits also involved adding image comparison tests to validate visualizer outputs and updating documentation to reflect changes and provide clear usage examples. Furthermore, the user revised and documented the threshold quick method.
pythonvisual-analysisvisualizermodel-selectionmachine-learning
PingThingsIO/pgimport
Apr 2021 - Aug 2021
Python-based tool for importing small datasets into BTrDB
Contributions:14 reviews, 3 PRs, 17 pushes in 4 months
datasetspythondatasetimporting
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