About

I'm a born statistician destined for finance, living in Tampa Bay, and most of my career has been about taking unstructured data and turning it into actionable insights. I discovered behavioral economics while studying for my B.Sc. at Florida State University, and I've applied its principles to all of my work since. I've learned financial services, manufacturing, logistics and politics from the inside, and built models for just about every corner of the CFO organization and beyond.

A model hands back a wrong number more confidently than a person does. Catching that is familiar work from managing a team of six procurement analysts: it comes from knowing the data well enough to see it. Over the past year I've made AI the way I work because I know where it breaks, and it runs on the same instinct as building a process up in Power Query. I describe rather than prescribe, and work that took days takes minutes. This site is built that way. The time goes into direction instead of into the build, and every measure, chart and page here is mine by design.

The part of the work I enjoy most is making money where it had been leaking before. Whether through loss avoidance using the predictive Risky Index, cash realization by completely rebuilding a hyper-accurate free cash flow forecast or talking down the State of Delaware over $2.7M in an unclaimed property audit, my analysis generates demonstrable capital outcomes for companies. Day to day, that work uses tools like Excel, Power BI and a lot of ETL across whatever ERPs a company happens to run.

Baseball for Humans started with a question I already knew how to answer somewhere else: if I could build an index for which customers were about to go sideways, why not one for how much a close loss hurts? That's where the Heartbreak Index came from. Every dashboard since has started the same way, on something fans feel that the box score doesn't say. When I'm not at a computer, I'm usually out on my bike or at the ballpark with my daughter.