I build models that find the taxpayer, transaction, or pattern that doesn't belong — anomaly detection and predictive modeling for the Dominican Republic's tax authority, and a growing body of independent ML work aimed at fintech.
I started in Bonao fixing hardware and walking people through unfamiliar software. Since then I've moved through banking analytics and into the Dominican Republic's tax authority, where I now build the models that decide which returns deserve a second look.
What hasn't changed is the instinct: take a pile of messy, high-volume data and find the thing in it that actually matters — the pattern, the leak, the outlier. I'm currently pushing that instinct further, working toward an ML engineering role in fintech, with GCP certifications and independent projects (sports prediction, anomaly detection, forecasting) alongside my day-to-day work.
The Dominican Republic's national tax authority, responsible for administering, regulating, and collecting all internal taxes nationwide.
Client and employer projects on the left; independent, open-source work — including the GCP-track and fintech-facing builds — below.
Leakage-safe rolling features and a logistic regression model predicting game outcomes.
61.3% accuracy vs. 50% baselineIsolation Forest applied to 526K property records to flag pricing anomalies.
526,000 recordsElectricity-consumption forecasting comparing Linear Regression and Random Forest.
forecasting · regressionElo ratings built from scratch plus attack/defense and player-form features; a Dixon-Coles Poisson + Monte Carlo phase is planned next.
in progress · phase 2 plannedCatholic and Technological University of Cibao (UCATECI)
01/2019 — 01/2025
Focused on designing tech solutions, project leadership, and process optimization — with an emphasis on efficiency and security in every project.