I'm a data scientist based in New York with a background that doesn't fit neatly into a single box — and I think that's the point. My career started in immigration law, moved through humanitarian emergency response, and has converged on machine learning and analytical systems.
That path gave me something most data scientists don't have: genuine fluency in the domains where data actually matters. I've worked with the case files, the clients, the constraints. When I build a model now, I understand what's at stake if it's wrong.
I hold an M.S. in Data Science from the New York Institute of Technology (Summa Cum Laude) and a B.A. in Economics & Political Science from Stony Brook University. My academic work focuses on supervised learning, clustering, and data engineering — with a particular interest in applications to economics, public policy, and legal operations.
I'm bilingual in English and Spanish, which has mattered in every professional context I've worked in.
Managed immigration caseloads before USCIS and immigration courts. Built a relational case management dashboard in Excel to track deadlines, case status, hearings, and document submissions across a diverse caseload.
Managed a complex humanitarian caseload at a large emergency response center. Built an optimization model for case assignment and resource allocation under legal, demographic, and operational constraints.
Managed asylum, removal defense, FOIA, and prosecutorial discretion caseloads before immigration courts, the Board of Immigration Appeals, USCIS, CBP, and DHS.