App
DocChat: Intelligent Document Retrieval & Verification for Technical Data
Self-correcting multi-agent RAG for audits. Built with LangGraph, Docling & Modal.
Professional Summary
I am a Data Scientist and Operations Research Analyst with over a decade of experience bridging the gap between rigorous statistical theory and frontier-tier AI implementation. My career has been defined by high-stakes environments—from conducting $20B+ cost assessments for the Air Force to building NLP-driven clustering models for brands like Chipotle.
I specialize in creating autonomous, self-improving systems that drive measurable business value:
Agentic AI & RAG: Architecting multi-agent systems with LangGraph, CrewAI, and LangChain to solve complex, unstructured enterprise problems.
Predictive Modeling: Developing high-accuracy models (85%+) for churn, demand forecasting, and cost analysis using Python and R.
Strategic Leadership: Leading $20B+ program assessments (TS/SCI Cleared) and mentoring data teams in SaaS and Government sectors.
Full-Stack Deployment: Building production-grade data tools and interactive dashboards using Streamlit, R-Shiny, and Databricks.
I thrive at the intersection of predictive analytics and agentic workflows, turning raw data into strategic, autonomous advantages.
Education
MS Statistics
California State University, Fullerton
BA Business Economics
University of California, Riverside
Interests
Experimental Design
Data Visualization
Product Development
Large Language Models
Recent Blogs
App
Integrated Retail Intelligence: Predictive Operations Control Center on Shiny Dashboards
ML-driven pricing & forecasting to end sales bottlenecks and accelerate time-to-close.
Notebook
Banking Retention Intelligence: A Statistical Audit of Customer Attrition
99% accurate churn model using SMOTENC & feature interaction engineering.
Notebook
R-Powered Strategic Retail Intelligence: From Performance Audit to Market R&D
DoD-grade auditability in retail ML: XGBoost pricing & UMAP high-dimensional discovery.