I turn complexity into actionable intelligence.
Founder & Principal Architect at Model Citizen. I work at the seam between raw, messy data and the decisions it's supposed to drive — building the systems that turn analysis into infrastructure teams can rely on. This page is the short version of how I think, how I work, what I think matters, and what that looks like in practice.
Data is only as valuable as the decisions it enables. I take decades of accumulated enterprise complexity and synthesize it into clear, defensible strategy. My mission is to ensure leadership never has to guess, providing the empirical foundation they need to lead with absolute confidence.
Define it once. Trust it everywhere.
Six principles I work by
Outcomes over outputs
Start from the decision, not the dataset.
A dashboard nobody acts on is decoration. I start from the decision that needs to get better and work backwards to the data — not the other way around.
Trust is the real deliverable
A number only counts if someone will defend it.
A number people believe beats a clever model they quietly ignore. Most of the work is making metrics legible, reconciled, and owned — so they survive scrutiny in the room where it counts.
Operationalize, don't just analyze
An insight in a notebook is a hobby.
Value shows up when the work runs reliably without me — scheduled, monitored, and resilient to the 3am page. I build analysis into infrastructure, not one-off artifacts.
Make the complex legible
Complexity is unavoidable; confusion is a choice.
I translate fluently between the warehouse and the boardroom. The hard part is rarely the math; it's turning a tangle of systems into a story a team can act on with confidence.
Build for the handoff
If it only works while I'm in the room, I've failed.
The best engagement leaves a team stronger than I found it — with patterns, documentation, and habits they own. I'm a conduit, not a dependency.
Pragmatism ships
The elegant system that never launches loses.
The boring solution that ships this quarter beats the elegant one that never lands. I optimize for momentum and maintainability over novelty for its own sake.
Have data that should be doing more?
Tell me about the pipeline that breaks, the metric nobody trusts, or the analysis stuck in a notebook. Let's operationalize it.