Bernease Herman is a data scientist and engineer with 11 years of experience translating academic machine learning research into practical, production-ready solutions across academia, startups, and enterprise. Based in Seattle, she has applied statistical and ML techniques to diverse domains—from public transit and wildfire modeling to SONAR and economic policy—while teaching workshops that bridge theory and practice. Her background includes hands-on software development at Amazon and collaborative research support at the University of Washington eScience Institute and WhyLabs, giving her a rare blend of research intuition and engineering discipline. She is particularly skilled at identifying the "final stretch" innovations and pragmatic hacks that make research deployable, and she approaches strategic career and product decisions with data-driven methods. Outside of work she pursues Olympic weightlifting, rowing, and DIY robotics projects, which inform a practical, experiment-driven mindset.
10 years of coding experience
12 years of employment as a software developer
Bachelor of Science (B.S.) Mathematics Statistics, Bachelor of Science (B.S.) Mathematics Statistics at University of Michigan
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