I started as a software engineer building mobile applications, focused on shipping reliable, well-structured products. Over time, the questions I found most interesting moved upstream — not just how a feature works, but what the data behind it says.
That curiosity led me into data science and machine learning. I enjoy exploratory data analysis: digging through a dataset to find patterns, anomalies, and the small inconsistencies that usually matter most.
I'm not interested in models for their own sake. I care about end-to-end products — the full path from raw data to something a user can actually open and use.
Software Engineer
2020 — Present- Build and ship Android apps in Kotlin, from the first estimate to the production release.
- On some projects, sit with the client directly to work out what actually needs building — not just what's in the spec.
- Test everything exhaustively before it ever reaches QA or the client — I'd rather find the bug than they would.
- Also wrote a data-science thesis in Python, chasing the same curiosity that runs through the rest of this site.
Finance Analytics
A personal analytics platform that turns a CSV export of bank transactions into spending trends, anomaly detection, recurring-subscription detection, and explainable insights — processed and stored entirely on-device, with every threshold documented and evidence-motivated rather than fitted to a model.
Event Impact AnalyticsIn progress
An observational study measuring whether NYC Yankees home games shift taxi activity around the stadium — built on a full year of NYC TLC Yellow Taxi trip records, taxi zone geometry, and the Yankees' 2019 schedule, framed around association rather than causal claims until the evidence supports more. Currently in the data-acquisition and validation stage: schema, quality and coverage checks on the taxi dataset are underway before any modeling begins.