Mingze Zhang is a Senior Data Scientist with 11 years of experience who blends a PhD-level command of statistics with hands-on ML/AI engineering to solve high-impact business problems. He has delivered production models that drove a $60M annual profit uplift at JPMorgan and cut ML runtime and costs by 80% through GPU-accelerated pipelines, deploying solutions on AWS EC2 and EMR. Skilled across Python, R, SAS, Spark and SQL, he designs interpretable, high-performing models (XGBoost, deep learning, NLP) and routinely applies techniques like monotonic constraints and SHAP for explainability. His research background in copula-based multivariate count time series translated into tangible gains—improving pandemic mortality forecasts and hurricane prediction—demonstrating the rare ability to move novel statistical methods into operational impact. Based in the NYC area, he combines strong communication and teaching experience with cross-functional collaboration to translate complex model insights into business decisions.
11 years of coding experience
7 years of employment as a software developer
Bachelor's degree, Statistics, 3.74/4.3, Bachelor's degree, Statistics, 3.74/4.3 at University of Science and Technology of China
Doctor of Philosophy - PhD, Statistics, 4.0/4.0, Doctor of Philosophy - PhD, Statistics, 4.0/4.0 at The George Washington University
Contributions:5 pushes, 4 branches in 5 years 3 months
libsecp256k1pythoncross-platformcffi
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