Yunhe J is a data scientist with a decade of professional experience and a strong statistical foundation from East China Normal University and a Master’s in Data Science from the University of San Francisco. He has applied ML and analytics across healthcare, retail, and finance, producing measurable impact such as an XGBoost model that cut hospital auditor costs by 80% and improving a bank promotion model AUC from 0.64 to 0.72. Currently at Guardian Life, he builds predictive models, monitoring frameworks, and reusable code modules to improve model reliability and team productivity. His background spans hands-on risk analysis at PayPal to production NLP and CNN work for contract analysis, showing comfort from feature engineering to deep learning. Based in Hangzhou with international experience, he blends rigorous applied statistics with pragmatic engineering to deploy high-impact, production-ready data solutions.
10 years of coding experience
4 years of employment as a software developer
Master's degree, Applied Statistics, Master's degree, Applied Statistics at East China Normal University
Master's degree, Data Science, Master's degree, Data Science at University of San Francisco
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