Now

This page is inspired by Derek Sivers' now page concept, a snapshot of what's actually occupying my attention rather than a static bio.

At Starz

I'm currently focused on expanding the content valuation infrastructure, moving from title-level ROI analysis to portfolio-level strategy. The question I'm most interested in: how do you optimize a content library when the value of any individual title depends on what else is in the library? That's a harder problem than it looks, and it's one of the genuinely interesting optimization challenges in the streaming space right now.

I'm also developing the next phase of our machine learning operations infrastructure, making model deployment and monitoring more systematic. The current stack centers on MLflow for experiment tracking, SageMaker for deployment, Airflow for orchestration, and GreatExpectations for data quality validation. We're also experimenting with marimo as a more reactive alternative to Jupyter for analytical workflows. The goal is less time on plumbing, more on the problems that actually matter.

Writing & Publishing

I'm actively building out the Data Science Rabbit Hole publication. The focus is on pieces that push back against the more absurd corners of AI hype while remaining genuinely useful to practitioners and executives. There's an almost unlimited supply of material.

I'm also writing for All Things Insights, where the audience skews more toward marketing and insights executives, which means translating the technical reality of data science into language that actually lands in a boardroom context.

Teaching

I'm teaching Marketing & Media Analytics at the University of Oklahoma this spring. The course evolves every cycle: the summer and fall go toward updating the material, incorporating student feedback, and keeping pace with a field that keeps moving. The goal is the same each time: helping students develop judgment that will outlast whatever specific tools are currently fashionable. Principles over frameworks. Frameworks over tools.

Thinking About

I'm increasingly interested in the question of organizational data maturity, not just technical capability but the decision-making culture that determines whether technical capability translates into business value. Most organizations that think they have a data science problem actually have a decision science problem, and the fix isn't more sophisticated models.

I'm also thinking about what the next five years of AI development actually means for organizations that are still figuring out how to use last decade's analytical tools. The gap between AI capability and AI deployment is growing, not shrinking. That gap is interesting to me both as a practitioner and as someone who writes about it.

What’s Next

I'm interested in problems at a larger scope: enterprise-level data strategy, AI governance, and the organizational design questions that determine whether advanced analytics creates durable competitive advantage or expensive overhead. If you're working on those problems and want to think through them together, I'm reachable by email.