The person
behind the models.

I started in restaurants. Five years running high-volume dining rooms in Houston taught me to think in systems before I had the language for it: labor, throughput, margin, the small levers that change a night's outcome. A Kinesiology degree in 2020 was supposed to point me toward physical therapy. The pandemic rerouted that, and the rerouting did me a favor. I wanted the business side, not the clinic.

The shift to marketing happened when I started paying attention to data. Watching a single pricing change move order mix, or one piece of content shift weekend traffic, was the lightbulb. I wanted to know which decisions were actually working and why. That question led me to build the tracking, automate the reporting, and use the data to make the next call instead of guessing. Sixteen months running SEO, paid social, and automation for Sushi One generated 340K organic impressions and moved the average position from 18.5 to 10.5.

The MMA at NC State's Jenkins School of Management adds the statistical rigor I could not get on my own: GLM, pricing strategy, conjoint analysis, supply chain optimization. The work is real, not academic. I built procurement analytics for a Fortune 500 commerce platform tracking $49M+ in spend. I delivered a B2B digital strategy for a North American industrial distributor. The case studies in this portfolio are those projects.

Outside coursework I run JVI, an independent build lab where I ship the tools I actually need: agent orchestration systems, automated data pipelines, and the analytics stack I use on real projects. Ten-plus repositories and counting. It is where I prove things to myself before I propose them to anyone else.

I keep coming back to one question across every domain I've worked in: what is the smallest input that changes the largest behavior? That question started in a kitchen, runs through every dashboard I've built, and is the reason I am pointing my career at marketing analytics teams that take attribution seriously. I want to work on revenue problems where the data is messy, the stakes are real, and the answer matters next quarter, not next year.