Summary
Sizhang Zhao is a Senior Machine Learning Engineer based in Seattle with nine years of experience building data-driven solutions across finance and tech, currently focused on production ML at DoorDash. He previously led advertising causal inference work as a Senior Applied Scientist at Amazon and developed quantitative trading models and statistical systems at Goldman Sachs and Wolfe Research. Sizhang blends strong statistical programming (R, MATLAB, Julia) with production engineering skills in Python, C++ and distributed systems, and holds advanced degrees in Financial Engineering and Machine Learning from Cornell and Georgia Tech. His background in chemical biology and economics gives him a multidisciplinary lens for feature engineering and model interpretability. A CFA Level II candidate and active GitHub maintainer, he pairs rigorous experimentation with deployment-savvy approaches to deliver measurable business impact. He has a track record of turning research-grade models into production pipelines that inform pricing, allocation, and ad-effectiveness decisions.
9 years of coding experience
7 years of employment as a software developer
Master's degree Machine Learning, Master's degree Machine Learning at Georgia Institute of Technology
Bachelor of Science (B.S.) Chemical Biology with double major in Economics, Bachelor of Science (B.S.) Chemical Biology with double major in Economics at Peking University
Master of Engineering (MEng) Financial Engineering, Master of Engineering (MEng) Financial Engineering at Cornell University
Chinese, English