Data Science is a data scientist with 11 years of experience and a 6-year focus on machine learning across technology, finance, and healthcare, combining rigorous MS training from UC Berkeley with hands-on product impact. He has driven measurable business outcomes—raising monthly retention by 11 percentage points, boosting conversions 16%, adding $240k annualized revenue via a production recommendation engine, and saving over $1M through prosthesis prototype analysis. Comfortable with structured and unstructured data, he codes in Python, R, and SQL, owns end-to-end workflows from feature engineering to deployment, and builds domain-specific tooling (notably packages for gastroenterological data on his GitHub). He blends analytical depth with cross-functional collaboration, turning experiments into operational products that improve lifetime value and pricing strategies. Always learning, he pursues applied research and practical open-source work that bridges clinical datasets and product analytics.
11 years of coding experience
2 years of employment as a software developer
Master of Science - MS, Data Processing, Master of Science - MS, Data Processing at University of California, Berkeley
Contributions:31 commits, 26 pushes, 1 branch in 3 months
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