I'm a statistics and computer science graduate who builds the pipelines, models, and visualizations that help teams and institutions see clearly and move with confidence — across public-sector, corporate, and consulting work alike.
I work at the intersection of statistics, software, and applied analytics. Across internships and research roles, the through-line has stayed the same: take messy, multi-source data and turn it into something a decision-maker can stand behind.
That's meant orchestrating ingestion pipelines from federal sources to study post-COVID migration, building file-validation systems that keep tens of thousands of records honest, and putting analyses in front of audiences like a county Chief Data Officer and an Economic Development Authority.
I care about the last mile of data work — the dashboard a director actually opens, the chart that reframes a debate, the validation routine that means nobody has to second-guess the numbers. Rigorous underneath, clear on the surface.
Orchestrated ingestion pipelines pulling from the FAO, U.S. Customs and Border Protection, and the Census Bureau — cleaning, standardizing, and deduplicating raw data to study post-COVID migration across the Northern Triangle, then connecting it to food-insecurity patterns for policy stakeholders.
Designed an evaluation dashboard measuring investment-program effectiveness across income distributions, letting leadership pinpoint target communities and track annual impact — with workflows automated through Python, SQL, and REST API integrations in a CI/CD environment.
Engineered a CSV validation program applying repeatable routines for validation, deduplication, and business-rule enforcement — ensuring the consistency and accuracy of 10,000 data files across many attributes and value ranges, and strengthening data integrity platform-wide.
Built a predictive classification model analyzing business diversity in Fairfax County — tuning SVM, Decision Tree, and Probit models with NLP techniques to track minority business ownership, and presenting findings to the County's Chief Data Officer and Economic Development Authority.
Led a multiple-regression study identifying which socioeconomic and policy factors predict infant mortality — running variable screening, nested F-tests, and weighted least squares in SAS to land on a defensible, interpretable model for public-health decision-makers.
I'm looking for data analyst, engineer, and scientist roles where rigorous work meets real impact. Happy to talk.