An aerospace engineer’s approach to enterprise AI.

I started in aerospace, where every component is connected and a decision in one area can ripple across performance, safety, cost, reliability, and operations. That training taught me to look beyond an individual feature and understand the full system required to create sustainable value.

AI and data products work the same way. A model is only as effective as the data pipelines, infrastructure, governance, user workflows, and business processes around it. At The Home Depot, I have applied that mindset across foundational data products, semantic layers, conversational analytics, and agentic decision workflows.

Trade studies, modeling, simulation, and testing taught me how to make decisions under real constraints: challenge assumptions, quantify uncertainty, compare alternatives, and balance performance against cost, latency, scalability, governance, and risk.

Hands-on experience designing, building, testing, and refining real systems also gave me a builder’s mindset. It helps me engage deeply with engineers and data scientists, translate technical constraints into clear product decisions, anticipate downstream impacts, and keep multidisciplinary teams focused on trustworthy, usable solutions that solve a real problem.