Data Scientist

I am an M.S. Physics candidate at the University of Central Florida, specializing in computational modeling and experimental data systems. My background combines rigorous physics training with practical experience building machine learning pipelines, predictive models, and data visualization tools.

I have worked on production deployments that processed more than 300,000 records and have contributed to peer-reviewed research across astrophysics, spintronics, and quantitative finance. I enjoy turning raw, complex data into structured, actionable insights that support better decisions.

My experience includes designing Python-based machine learning workflows, automating measurement protocols, and developing predictive data products for research teams. I have also built preprocessing pipelines for unstructured simulation outputs and improved model performance through targeted methodology refinements.

In quantitative finance, I designed an evaluation framework for options data, applied stochastic modeling and backtesting, and delivered production-ready predictive models. I value analytical discipline, statistical rigor, and the ability to validate ideas with real data.

I also built an end-to-end public analytics tool for U.S. gun violence intelligence, integrating CDC records, statistical modeling, and interactive visualization into a deployed platform. This project reflects my ability to manage the full data lifecycle from ingestion to delivery.

Overall, I bring a cross-domain perspective that blends physics, machine learning, and data analytics. I am motivated by challenging problems, collaborative research, and building systems that are both technically sound and practically useful.