Paul Nunez is a Senior Data Scientist and machine learning engineer with nine years of experience building ad retrieval and targeting systems at high-growth tech firms in the Bay Area. He holds a PhD in Chemistry with a machine learning minor from Caltech and has a track record of translating research into production: at Pinterest he improved actalike ad targeting and raised offline evaluation robustness, and he now focuses on ads at Faire. His background bridging experimental physical sciences and ML gives him uncommon strength in designing data-driven systems that handle noisy, real-world signals. Comfortable across research and engineering, he combines deep probabilistic reasoning with pragmatic model deployment to drive measurable ad performance improvements.
9 years of coding experience
3 years of employment as a software developer
Bachelor of Science (B.S.) Chemical Engineering, Bachelor of Science (B.S.) Chemical Engineering at The University of Texas at Austin
Contributions:85 pushes, 1 branch in 1 year 2 months
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