I build end-to-end machine learning — from ETL and feature engineering to models, agents, and the platforms that serve them.
I started my career at Nielsen on a global R&D team specializing in forecasting. My most notable work there was a modeling platform that automated the Box-Jenkins (ARIMA) methodology — it won P&G's global forecasting business and led to four patent applications. That project sparked a deep interest in predictive modeling and applying it where it creates real value.
Since then I've worked across credit risk, banking, advertising technology, and consulting — building origination and fraud scorecards, CCAR/DFAST stress-testing models, auto-ML forecasting engines, and most recently generative-AI accelerators: multi-tenant Azure platforms, LLM tool-calling agents, multimodal RAG, and MCP servers. Today I'm an AI/ML Architect at Protiviti in Chicago.
I hold a BS and MS in Economics from UNC Charlotte, a post-graduate program in AI/ML from UT Austin, and I'm pursuing a second MS in Artificial Intelligence at Johns Hopkins University. Explore my portfolio for projects, skills, and certificates.
Outside of work I read, tinker with my rackmount homelab, kayak when the weather allows, and play volleyball when I can get on a team. I've also delivered a number of independent consulting engagements — reach out if your organization needs data-science firepower.
