I analyze user behavior, build predictive models, and design decision frameworks that help teams move with confidence. My work blends statistical rigor with product intuition, focusing on impact and clarity.
I’m an analytics and data science professional at Purdue University, focused on making data solutions usable for real teams. I love turning qualitative insights into measurable hypotheses, and building the pipelines, models, and dashboards that close the loop between analysis and decision.
My approach emphasizes transparent evaluation, systems thinking, and growth. I care about reliability, clarity, and helping teams see the signal in the noise—then act on it.
Trained a gradient boosting model to predict conversion propensity and inform targeting. Deployed explanations to surface drivers like shipping thresholds and page speed.
Built a classification ensemble to place surviviors of deceased veterans in to an appropriate TAPS grief stage.
Personal/hobby project focused on clustering NBA players on season averages from the 2024-2025 season.
Partner with product and marketing to analyze user journeys, build dashboards, and guide test decisions. Focus on clarity, measurement, and practical impact.
Contributed to analytics initiatives, improved reporting reliability, and supported cross‑functional teams with clear insights and documentation.
Developed SQL queries for assisting customers issue for enterprise Guaranteed Asset Protection products and wrote PowerShell scripts to automate QA bot server directory clean-up.
Coursework in data science, statistics, database systems, and analytics strategy. Active in projects connecting quantitative analysis to business outcomes.
Interested in collaborating or chatting about analytics at scale? Reach out—I’m always open to practical problems and measurable impact.