About

For the first decade of my career, I advanced through data analytics roles — eventually leading a team — and kept finding myself in the same place: between the people who understood the business and the people who could build for it.

I wasn't a data scientist. I didn't try to be. Instead, I learned enough about both worlds to make them productive together. I called it being an analytics translator. McKinsey would later publish the term in Harvard Business Review as "the new must-have role." I was already living it.

What made it real wasn't the label — it was a habit I've carried at every stage: deliberately investing in the adjacent competency, the layer that wasn't in my job description but that made the core contribution more powerful. As an analyst, that meant genuine fluency in data science methods and the business domain. Now, it means hands-on prototyping of AI capabilities without an engineering title. The job description has never been the ceiling.

AI has changed the shape of the gap, not the instinct. The translation layer between domain expertise and technical execution is collapsing. The scarce resource isn't execution anymore. It's judgment — knowing which problems are worth solving, which are the right shape for AI, and how to build toward them without losing what the business actually needs.

In 2025, I joined the innovation lab at a Fortune 100 P&C insurer, where I prototype agentic AI and GenAI solutions for the property and casualty insurance domain. I'm not an engineer. But I've invested in understanding how these systems work from the inside — because meaningful dialogue with builders requires it. A colleague recently described me as someone who can engage the non-technical big picture and dive into the technical AI weeds with equal fluency, in the same conversation. That range is something I've built on purpose.

What grounds all of it is something harder to name: a practiced ability to imagine being the person on the other end. The end user. The business stakeholder. The person who will actually use this. Good AI work requires knowing what's technically possible and what a real person actually needs — and holding both at once.

That's the gap I've always lived in. It just keeps moving.

Problem Shape Recognition
The ability to sense which business problems are genuinely suited to AI solutions — and which aren't. This is harder than it sounds and more valuable than execution. Before you can build the right thing, someone has to ask the right question.

Full-Register Fluency
Equally comfortable in the strategic big picture with non-technical stakeholders and in the technical weeds with engineers. Not many people can move between those registers in the same conversation. I've built that range deliberately.

Deliberate Adjacency
At every stage, I've invested in the layer that wasn't required. As an analyst: data science fluency and domain depth. In my current role: firsthand AI prototyping without an engineering title. The job description has never been the ceiling.

Domain-Grounded AI Literacy
Ten years in data analytics, now applied to LLMs and agentic systems. Not engineering — but genuine, firsthand familiarity with how these systems work, where they fail, and how to direct them toward problems worth solving.

End User Empathy
A practiced instinct for imagining the person on the other end of the work. In an era when it's easy to optimize for what's technically impressive, this is what keeps the human in focus. It's what made the analytics translator identity work, and it's what makes the AI work worth doing.

High-Agency Orientation
A bias toward building, shipping, and learning rather than waiting for structures to carry the work forward. The future belongs to people who combine domain depth with the agency to act on it. That's the combination I'm building toward.

2014–2015 · Economic Consulting
Started out in economic consulting — experience with econometrics and data analysis in a variety of industry contexts.

2016–2024 · Data Analytics → Team Leadership
Advanced through analytics roles at a Fortune 100 P&C insurer, ultimately managing a data analytics team. Practiced the analytics translator identity before it had a name — bridging technical practitioners and business stakeholders. Invested in data science fluency and domain expertise beyond what the role required.

2018 · External Validation
McKinsey publishes "Analytics Translator: The New Must-Have Role" in Harvard Business Review. Confirmation of what was already a lived practice.

2025–Present · Innovation Lab, Fortune 100 P&C Insurer
Joined a dedicated innovation lab. Prototyping agentic AI and GenAI solutions for the property and casualty insurance domain — without an engineering title, by design. Analytics translator becomes AI Opportunity Architect. Same gap. New tools. Higher stakes.

MBA