Irving, Texas, United States
Most of my work orbits a single question: Did this ad actually work? It seems simple enough. Then you get past the last click and realize the answer is rarely clean. As a Senior Data Scientist, I spend my time at the messy intersection of ad tech, measurement, and causal inference, where the real job isn't counting outcomes, it's figuring out which ones you actually caused. That means living in the tension between attribution and incrementality. Attribution tells you where credit usually goes. Incrementality tells you where value was actually created. They don't always agree, and closing that gap is where the interesting problems live. I lean on A/B testing, geo-experiments, and causal models to help brands bet on what moves the needle, not just what looks good in a dashboard. Alongside the measurement work, I've spent a lot of time in audience analytics and retail media. Not watching from the sidelines, but building. First-party data is the asset everyone talks about, but turning it into something brands actually want to buy is a different skill set. Building smart audience segments is the easy part. The harder part is packaging that intelligence into a story that lands with a brand partner, tightens the relationship between retailer and supplier, and actually drives business. In this ecosystem, the audience is the product. Knowing where that leverage sits matters. And then there's the AI thread. I've been digging into how large language models change the way we interact with measurement. I'm less interested in fancier dashboards and more interested in systems where someone can ask the data a question, push back on the answer, and explore it conversationally, without the rigor falling apart in the process. Open to speaking opportunities, panel discussions, and podcast features regarding causal modeling, experimentation, and marketing analytics.
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Student Inspector (University Housing Operations) & Student Worker (Dining Services), UT Dallas
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