Applied Data. Marketing Science. Python. AI on the Edge.
Data Scientist with expertise in Marketing Mix Models, campaign optimization, and full-funnel marketing analytics. Outside work, I chase alternative datasets like football, theme parks, prediction markets and trend analysis in platforms like Youtube, TikTok and Reddit. Real-world events are the messiest, most interesting modeling problems there are.
Most data scientists hand their models off at the door. I started on the other side of that door.
Before the Python, the MMM models and the statistics, I was thinking in campaigns, briefs, creatives, channel strategy.
Then a master's degree pulled me toward the science, and I realized I had something most people don't: I could see marketing the way a data scientist does, and data the way a marketer does.
For eight years, six of them embedded inside marketing teams, I've used that lens to optimize spend across channels, connect what the numbers say to what teams actually do, and drag insights all the way from "huh, interesting" to "let's ship it."
The model isn't the work. Getting it used is the work.
01★ featuredA live wait-time dashboard for Universal Orlando. A cron collector polls themeparks.wiki every minute into SQLite; a FastAPI app then charts how ride queues move across the day, week, and month — with weather overlaid on the trend.
02★ featuredA double-entry personal finance app with a keyboard-first interface and zero cloud lock-in.
03★ featuredMarkdown notes that sync over plain files — no database, no accounts, just a folder you own.
04★ featuredThe 2026 World Cup in data — every one of the 48 squads and 1,248 players scraped from FIFA, then enriched with birthplaces and full club-career histories and normalized into a queryable SQLite database. Explored live on this site.
Durability is not a feature you add at the end. It is a hundred small refusals made along the way.
The best tools I have built were the ones nobody asked for, made in an afternoon, used for years.