Min Chen is a senior analytics auditor and data scientist with a decade of experience turning messy text and disparate data sources into actionable intelligence for marketing, insurance, and pharmaceutical clients. She blends deep expertise in NLP, computational linguistics, and predictive modeling with hands-on BI and ETL design, having built solutions from automated pharmacovigilance pipelines to donation-predictive models that cut campaign costs. Min has led cross-functional teams and client-facing analytics at firms like Assurant, Megaputer, and CE Strategy, and she pairs strong academic rigor (MS in Data Science, MS in Economics) with a track record of operationalizing models into production. Notably, her work spans advanced text tasks (anaphora resolution, semantic mapping, graph databases) and practical wins such as reducing direct-mail spend by 23% and automating adverse-event extraction to regulatory standards. Colleagues describe her as a pragmatic problem-solver who mentors others while designing robust, auditable analytics systems.
10 years of coding experience
1 year of employment as a software developer
Bachelor of Business Administration - BBA, Accountancy and Applied Mathematics, 3.65, Bachelor of Business Administration - BBA, Accountancy and Applied Mathematics, 3.65 at The Hong Kong Polytechnic University
Master of Science - MS, Data Science, 4.0, Master of Science - MS, Data Science, 4.0 at Indiana University Bloomington
Master of Science - MS, Economics, 3.82, Master of Science - MS, Economics, 3.82 at Arizona State University - W. P. Carey School of Business
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