AI/ML Engineer & Data Scientist
Background
I'm a final-year CS and Economics double-major with hands-on experience deploying ML systems, with an intense focus on delivering business value. I care as much about why a model works as whether it does — and I'm drawn to problems where messy real-world data needs to be shaped into something that actually generalizes.
Beyond the technical, my economics background helps me understand customer needs and communicate technical concepts clearly to non-technical users.
Selected Work
Highlights across internship, school, and personal work — each with a full write-up.
Agentic RAG chatbot deployed on Databricks for CoorsTek's R&D group — connecting engineers to their data via orchestrated AI agents. Built from scratch to first production deployment in 2 months.
Production CV system for automated quality control at CoorsTek — improved defect detection accuracy from ~80% to 99% by fixing data quality issues and overhauling the model development procedure.
Deep learning pipeline for pixel-level analysis of ceramic part geometry from XCT images — enabling automated comparison of green-formed and fired parts against CAD spec without manual measurement.
A web app that tracks your experience and job applications, then uses a local LLM to generate a tailored resume and cover letter for each position — built to ship a real AI pipeline quickly in unfamiliar territory.
Read More →ML model trained on Ames, Iowa housing data to predict a home's after-renovation value — motivated by a contractor's need for fast, data-driven ARV estimates before committing to a job.
Read More →Experience
Three consecutive internships over 14 months at one of the world's largest advanced ceramics manufacturers. Built and deployed four production ML systems: semantic segmentation tools for ceramic part analysis, a graph-based material history system, an agentic RAG chatbot (10× faster data retrieval for R&D engineers), and a production defect detection pipeline (improved accuracy from ~80% to 99%).
Background
Rochester Institute of Technology — Rochester, NY
Double major at RIT combining a rigorous CS foundation — ML, deep learning, NLP, algorithms, systems — with quantitative economics in econometrics, game theory, and financial theory. The combination enables me to build models that are technically sound and economically interpretable, a pairing that has proven uniquely valuable across every project I've worked on.
Academic
Two papers in progress — one currently under peer review.
An economic model of a waste-managing firm that chooses to undergo R&D to increase the efficiency of their waste disposal — examining how AI adoption reshapes the firm's investment and profit maximization problem.
Identifies a relatively novel IPR-based subsidy that aims to bring privately optimal levels of R&D into balance with social optimum, comparing it against traditional input- and output-based subsidy approaches.
Writing
Research papers and essays on AI, machine learning policy, and economics.
Get in Touch
Open to full-time roles in AI/ML engineering and data science starting January 2027.