Vincent Rupp is a data scientist with nearly a decade of experience turning messy enterprise data into actionable models and visualizations, most recently building ML pipelines and graph-based customer segmentation at U-Haul. He combines classical statistical rigor (t-tests, chi-sq) and actuarial-informed analytics with modern ML techniques—XGBoost, random forests, PCA/t-SNE, LSH—and has presented findings to C-suite stakeholders. Comfortable both coding production solutions (Databricks notebooks, Neo4j graph models, SQL, SAS) and teaching complex concepts—he has a long adjunct teaching record creating courses and instructional media. Vincent also experiments with agentic LLM tooling to summarize and query past notebooks, reflecting a practical interest in developer-facing AI UX. His background in mathematics and actuarial work gives him a strong foundation for robust, interpretable modeling in messy real-world domains.
10 years of coding experience
5 years of employment as a software developer
Master’s Degree, Mathematics and Statistics, Master’s Degree, Mathematics and Statistics at Northeastern University
Non-degree seeking student, CADD, Calligraphy, Non-degree seeking student, CADD, Calligraphy at Portland Community College
Certificate through Coursera, Machine Learning, Certificate through Coursera, Machine Learning at Johns Hopkins Bloomberg School of Public Health
NLP Specialization Certificate, NLP Specialization Certificate at DeepLearning.ai
Bachelor's degree, Mathematics, Bachelor's degree, Mathematics at University of Colorado Boulder
Certificate, Machine Learning, Certificate, Machine Learning at Stanford University
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