Summary
Li-pin Juan is a Principal Data Scientist with nine years of experience blending economics, applied statistics, and production-grade Python engineering to build robust quantitative solutions for financial and retail enterprises. Trained as an economist with a PhD in Applied Economics and an MS in Statistics, he has moved from academic macroeconomic modeling and high-performance optimization to enterprise model governance and agentic AI workflow development at Freddie Mac and Fidelity. At Lowe’s he led severe-weather intelligence analytics, and his Federal Reserve and academic research roots reflect strong data engineering, simulation, and kernel-based estimation skills. Comfortable translating complex econometric models into scalable code (MPI/OpenMP, SQL, R, STATA, C++), he’s equally adept at governance and productionization—an unusual mix that helps bridge research rigor and operational reliability. Based in Boston, he brings a disciplined, systems-level perspective to building auditable, high-impact analytics for regulated industries.
9 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy - PhD, Applied Economics, Doctor of Philosophy - PhD, Applied Economics at University of Minnesota
Master's degree - MS, Statistics, Master's degree - MS, Statistics at Georgia Institute of Technology
Master of Business Administration - MBA, Financial Engineering, Master of Business Administration - MBA, Financial Engineering at National Taiwan University
Bachelor's degree - BS, Power Mechanical Engineering, Bachelor's degree - BS, Power Mechanical Engineering at National Tsing Hua University