Wes Barnett is a Machine Learning Engineer with 11 years of experience building end-to-end ML products and MLOps solutions using Python, SQL, and Docker, grounded in strong statistical and engineering foundations. He holds a PhD in Chemical and Biomolecular Engineering from Tulane and complements his technical training with a Master of Divinity, reflecting an unusual blend of rigorous research and human-centered perspective. Comfortable moving models from prototype to production, Wes has worked across academia and industry to translate complex data into actionable business outcomes. Based in the New York City area, he brings deep analytical rigor and systems thinking to ML engineering challenges, with a particular interest in making robust, reproducible pipelines that scale.
11 years of coding experience
Master of Divinity - MDiv, 4.00/4.00, Master of Divinity - MDiv, 4.00/4.00 at New Orleans Baptist Theological Seminary
Doctor of Philosophy - PhD, Chemical and Biomolecular Engineering, 3.94/4.00, Doctor of Philosophy - PhD, Chemical and Biomolecular Engineering, 3.94/4.00 at Tulane University
Bachelor of Science - BS, Mechanical Engineering, 3.78/4.00, Bachelor of Science - BS, Mechanical Engineering, 3.78/4.00 at Mississippi State University
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Contributions:30 pushes, 2 branches in 10 months
polarspythondatalabeled-datamanipulation
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