Effy Tang is a Master’s student in the Michigan Program in Survey and Data Science at the University of Michigan. She holds a bachelor’s degree in mathematics and economics from the College of Literature, Science, and the Arts and a master’s degree in supply chain management from the Stephen M. Ross School of Business. This combined training provides her with a strong quantitative foundation and applied expertise in operational and supply chain analytics. Her interests focus on integrating survey data, statistical modeling, and operational data to support data driven decision making in both policy and industry settings.
She currently serves as a Graduate Student Instructor in the Department of Economics, where she teaches advanced statistical inference and supports Stata based econometric analysis. Effy also works as a research assistant at the Institute for Social Research, analyzing large scale survey datasets for social policy research. In parallel, she has extensive experience in supply chain analytics through industry consulting projects, where she built Power BI dashboards, automated Python data pipelines, and delivered actionable insights across large scale logistics and distribution systems. After graduation, she plans to pursue a career in applied data science or analytics, bridging rigorous quantitative methods with real world supply chain and survey applications.
