AI/ML Engineer & Data Scientist — turning complex problems into verifiable solutions
From supply chain optimization to real-time arbitrage engines, I build systems where outcomes are measurable, decisions are data-driven, and AI isn't just a tool—it's the architecture.
Not just code—curiosity, failure, and relentless building.
I started with a simple belief: data should drive decisions, not guesswork. That belief took me from BYU-Idaho's data science program to United Airlines' cybersecurity team, where I reduced pipeline runtimes by 80% and scaled systems 10x.
At WPA Intelligence, I built ML models predicting voter behavior with 90%+ accuracy across 25 campaigns. At Tree Top, I'm designing linear programming models to optimize fruit supply chains—saving real dollars in transportation and warehousing.
But my real passion? Agentic systems. I'm not a "vibe coder." I architect AI that reasons—like Compa, my real-time arbitrage engine that combines LLM reasoning with structured domain knowledge to find profitable flips before anyone else.
25+ projects. Some failed. All taught me something. The ones that worked? They verify their outcomes in dollars, votes, or efficiency gains.
Impact measured in numbers, not buzzwords.
Agentic systems, ML pipelines, and tools that verify their own success.
Real-time arbitrage engine for Facebook Marketplace. LLM-powered title normalization, eBay sold comp fetching, query refinement, and profit calculation—all in a Chrome extension with a docked rail UI.
Automated watch arbitrage system. Scrapes watch listings, identifies undervalued pieces using ML price estimation, and alerts on profitable opportunities.
TypeScript-based field data collection and analysis tool. Streamlines agricultural data gathering with structured forms and real-time validation.
Advanced time series analysis toolkit with dynamic clustering. Processes temporal data to identify patterns, anomalies, and forecasting signals.
Analyzed 8M+ rows of voter data, applied ML to predict party affiliation and turnout. Earned 1st Place at 2023 I-Hack Hackathon.
Time Series Document Clustering on 5,000 OSHA documents. Used NLP and unique clustering to track topic trends, emergence, and seasonal significance.
Interactive visualization web app using Canada and US census data. Filters and maps to identify and target new markets for business expansion.
Calculus 3 learning project implementing neural networks with Jacobian matrices. Deep understanding of backpropagation through matrix calculus.
Languages, frameworks, and domains I've mastered through real projects.
Documenting the journey from data to decisions.
I'm looking for AI/ML Engineering or Data Science roles where I can turn complex problems into verifiable solutions. If that sounds like your team, let's talk.