Marketing analytics, data engineering, and machine learning — from attribution and forecasting to automated pipelines and LLM systems. M.S. Data Science, University of North Texas (May 2026) — now open to full-time roles.
I built a decision-ready fraud detection system that combines BERT NLP, anomaly detection, and SHAP explainability to flag suspicious job listings with interpretable risk signals.
I engineered the data pipeline behind a 7.7M-record safety analytics system, powering risk clusters, severity models, and an interactive advisory tool for logistics and insurance use cases.
I built the decision-ready retrieval backbone for a deep learning RAG agent, engineering ingestion, vector search, and orchestration so users could get grounded answers with source context.
I'm a business-minded data scientist who treats every model as a decision someone downstream has to trust and act on. I care as much about the clarity of the finding as the accuracy of the number — which usually means a clean pipeline, a sharp visual, and a story that survives contact with a stakeholder meeting.
M.S. Data Science · UNT · May 2026Recently completed my M.S. in Data Science and my capstone at UNT — now looking for a full-time Data Science, Data Engineering, or Marketing Analytics role where I can ship decision-ready systems from day one.
Wrapped my Master's capstone — a hybrid BERT + Isolation Forest fraud detection system for digital recruitment platforms with SHAP-driven explainability, reaching 99% accuracy and a 0.9718 ROC-AUC.
View case study →Outside of data: sewing, painting, and trails — habits that sharpen the pattern recognition I bring to every analysis. More about me →