About

I learned the methods through mathematics and econometrics, then learned the work through client decisions.

What I do

I've been a data scientist for 4+ years, since November 2021: first at Ringer Sciences and, since 2024, as an independent consultant. Most of my work sits where marketing, customer behavior, and data systems meet: measuring what each marketing channel adds, forecasting demand, running fair tests, analyzing text such as reviews and social posts, and building the data pipelines that keep reports running week after week. I like both halves of the job, talking through model internals with technical people and telling a business owner what a result means for them.

Most of my work starts with a person, not a dataset. I ask what decision the numbers need to support, cost a few options before building anything, and agree on what counts as a lead or a sale before I look at results. When people disagree, I'd rather run a test than argue. When I report back, I say plainly what the data can't show yet.

I studied at the University of South Florida and earned a B.A. in Mathematics and a B.S. in Econometrics and Quantitative Economics. While studying, I spent nearly four years in computer sales at Best Buy, explaining technical products to customers. I left school knowing R, Stata, and a lot of statistics, and taught myself Python and SQL because that's what industry runs on. A C++ scientific computing library with Python bindings, SicLib, helped get me my first data science job.

Ringer Sciences, 2021 to 2024

From November 2021 to February 2024 I was a Data Scientist at Ringer Sciences, a marketing analytics consultancy with a small, hands-on team. I reported to the head data scientist and worked across six client engagements, and it's where I learned marketing data science and client management.

I built and ran daily pipelines that read more than 2 million social posts and sorted them by tone, topic, and the brands or people they mention (sentiment analysis, entity recognition, and text classification). Client teams changed media and content plans based on the findings, and the work became a product sold to other clients.
For a large hospital client, I pulled hand-typed journal ad data from Google Sheets into BigQuery, joined it with ad platform data, and built dashboards for marketers and physicians. They became a recurring paid product and informed budget reallocation. It's still my favorite project because it ran end to end, from messy spreadsheets to takeaways doctors could use.
I built a search assistant over a B2B media client's own reports. Over a one-quarter pilot, its marketing and PR department reported about 40% less research time, and the client kept it as a recurring paid product.
I developed marketing mix models, forecasting models, and A/B test frameworks that teams used to set channel budgets. The forecasts were about 18% more accurate.
I automated 12 recurring analyses, saving analysts about six hours a week, and packaged deliverables so repeat work needed up to 90% less manual effort. On some clients I built the data models in dbt.

Independent consulting, 2024 to now

Since February 2024 I've worked with food and beverage, agriculture, and consumer businesses. In a small business, I'm often the only analytics person, so the job runs from campaign setup to the owner's decision. One client, a cafe that became an event lounge, kept me on retainer as its marketing data scientist.

I tied paid campaigns to verified visits with dated flyers, phone numbers, and personal offer codes. On lead campaigns, the ad spend behind each dollar of tracked sales fell about 70% after I rebuilt the audiences from real visitors.
I found demand for a new event format by analyzing Reddit, Meetup, and group chats. Foot traffic doubled within one quarter of the launch.
I built a lead CRM for a wholesale supplier's sales team and a model on top of it that ranks which leads to call first, running on Google Cloud. Closing rate per call rose about 50% once the team called the strongest leads first.
For another client, I scored prospects from business records, and lead conversion rose about 30% against the period before, once the scoring chose who the team contacted.
I picked a two-pastry bundle from POS basket analysis, and the average ticket ran about 30% higher after it was introduced, a before-and-after comparison.
I train Bayesian marketing mix models, which estimate what each channel adds, run Google Ads and Meta Ads funnels, and forecast visits by hour so owners know what a given spend means for staff and stock.

AI systems and agents

I use language models where they save writing or search time, and I keep them away from the math. Calculations happen in Python against the source data, and the model writes around numbers that are already computed. My economic analytics project shows the pattern: after I moved the calculation out of the model, all 21 current insights passed a database check.

I use Claude Code and Codex as coding assistants for routine data loading and dashboard code. The analysis, the design, and the decisions about what matters to the business stay with me, and I review everything they produce. The technical work page has the technical detail.

Outside work

I paint while traveling because it makes me look at a place more slowly than a camera does. The painting pages include work from Mongolia, Kyrgyzstan, Chile, and Mount St. Helens.