Xiaohan Wang is a Beijing-based data scientist with eight years of experience applying machine learning, statistics, and business intelligence to product and finance problems at ByteDance, Kuaishou, JD.com and quantitative investment firms. Trained at Beijing Institute of Technology and UC Berkeley (M.Eng IEOR), she has built demand-forecasting and inventory-optimization models that reduced JD.COM’s redundant inventory by 13% and developed macro-default and stock-performance predictors with >80% accuracy. Skilled in Python, SQL and feature engineering, she bridges research-grade modeling and production requirements—turning sparse or new-product data into actionable forecasts. At large platforms she has delivered practical solutions for ad/retail workflows, and her GitHub focus on deep learning systems hints at a growing interest in scalable ML infrastructure.
8 years of coding experience
5 years of employment as a software developer
Master of Engineering - MEng Industrial Engineering and Operations Research, Master of Engineering - MEng Industrial Engineering and Operations Research at University of California, Berkeley
Statistics, Statistics at Beijing Institute of Technology
Contributions:12 pushes, 1 branch in 1 year 2 months
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