About
I’m an enterprise Data, Analytics & AI executive based in New Hampshire. I’ve spent twenty years in this field, long enough to have built things from scratch, inherited things that were broken, and led organizations through the kind of modernization efforts that look straightforward on a slide and aren’t.
My background spans SaaS, consumer products, retail, and distribution. The industries vary. The underlying challenge is usually the same: the data exists, the business needs it, and something structural is getting in the way.
What I’ve done
At SharkNinja I own the global Data and AI function across commercial, supply chain, and product. Before that, at Veracode, I ran enterprise data and analytics through a transformation period focused on revenue forecasting, customer intelligence, and governance in a regulated environment. At Bonterra I started the Data Products organization from one person and built it into a team spanning analytics, data science, and platform work. We turned embedded analytics into a product line that generated around three million in incremental ARR.
Across those roles I’ve managed roughly ten million in annual operating budget, led distributed teams across engineering, analytics, and data science, and owned the vendor relationships and contract negotiations that don’t show up on roadmaps but matter a lot in practice.
How I think about this work
A few things I’ve come to believe through direct experience rather than theory:
Most analytics dysfunction traces back to org design, not talent. The question of who controls analytical resources and what they can see from that position determines what work gets done. When the structure is wrong, capable people still can’t fix it.
Governance is what separates AI experiments from AI that scales. Getting a model into production is the easy part. Building the access controls, monitoring, accountability structures, and decision frameworks that let an organization deploy AI confidently across multiple use cases is where the real work is.
Data capability compounds, but only when you build it that way. Organizations that stay behind on data aren’t usually behind on tools. They’re behind because they never built a foundation where each new use case gets cheaper and faster than the last.
I’ve also learned to be skeptical of scaling teams before the structure is ready for it. Larger organizations don’t always mean more output.
What I’m looking for
A VP or SVP role with full ownership of the Data, Analytics and AI function. I’m looking for an environment where that function is expected to drive business outcomes, not just support them.