Mark Lin
Baseball Analytics Associate
Shanghai, United States
Summary
🤩
Rockstar🎓
Top SchoolMark 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 WorldwidePostbaccalaureate Studies, General Studies, Postbaccalaureate Studies, General Studies at Columbia University in the City of New YorkMathematics and Science Resources Class, Mathematics and Science, Mathematics and Science Resources Class, Mathematics and Science at National Chiayi Senior High SchoolBachelor 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
English, Chinese, Korean
Github Skills (64)
keras-tensorflow10
r10
java10
hyperparameter-optimization10
graph-embedding10
automl10
automated-machine-learning10
neural-architecture-search10
spark9
algorithm9
large-scale9
python9
scala9
jdbc9
machine-learning9
Programming languages (5)
Github contributions (5)
hibayesian/spark-lof
May 2017 - Jan 2022
A parallel implementation of local outlier factor based on Spark
Contributions:5 commits, 1 PR, 4 pushes in 4 years 9 months
sparkmachine-learningoutlier-detectionlocal-outlier-factor
hibayesian/awesome-automl-papers
Nov 2017 - Nov 2022
A curated list of automated machine learning papers, articles, tutorials, slides and projects
Contributions:148 commits, 50 PRs, 149 pushes in 5 years
automated-machine-learningautomlautomated-feature-engineeringhyperparameter-optimizationneural-architecture-search
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