Mark Lin is a Baseball Analytics Associate with nine years of quantitative and applied-algorithm experience, currently shaping data-driven decision making for the Detroit Tigers. He blends an academic foundation in applied analytics from Columbia with practical baseball player development certification to turn complex models into actionable scouting and performance insights. His background spans interdisciplinary research in probabilistic programming and modular Bayesian inference as well as hands-on work building VBA tools, K-means clustering, and visual analyses to uncover hidden player patterns. Mark has contributed to the baseball analytics community via SABR, freelance writing, and conference presentations, demonstrating an ability to communicate technical findings to practitioners. Comfortable across research, consulting and product-facing roles, he pairs mathematical rigor with domain knowledge in sports science and economics. A multilingual, internationally trained analyst based in Shanghai, he brings both global perspective and a penchant for elegant, algorithmic solutions to baseball problems.
9 years of coding experience
1 year of employment as a software developer
Certificate, Baseball Player Development, Certificate, Baseball Player Development at Sports Management Worldwide
Postbaccalaureate Studies, General Studies, Postbaccalaureate Studies, General Studies at Columbia University in the City of New York
Mathematics and Science Resources Class, Mathematics and Science, Mathematics and Science Resources Class, Mathematics and Science at National Chiayi Senior High School
Bachelor of Business Administration - BBA, Business Economics; Minor in Korean Studies, Bachelor of Business Administration - BBA, Business Economics; Minor in Korean Studies at City University of Hong Kong
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