I am a Senior Specialist, Learning and Development at Organon, and before that I was a Clinical Systems Analyst I at Massachusetts General Hospital in Boston. Across both, the thread in my work is data: SQL reporting out of clinical systems, regulatory information systems and databases, data integrity, AI workflow automation — and, on my own time, data models I design, build, and publish. This portfolio collects that work the way a data architect would look at it: sources, quality, access, and use. My degree is in Management Information Systems (UMass Dartmouth, 2022), which is where the systems-and-data thinking started.
At Mass General I built regulatory reports and data summaries for clinicians through EPIC and SQL, and SQL reports on Vocera device usage by department — and worked the data-quality side, making sure data was captured correctly in the first place.
At Organon I analyze and optimize regulatory information systems and databases, work the ORacLe library collection that researchers draw on, build tools like the Journal Finder ($10K+ in vendor cost avoided), and automate documentation workflows with AI.
My own public data projects, published as @dawgometer: the Dawgometer, a custom multi-factor model that rates NHL skaters on a single scale; weekly power rankings in three model variants; and a Monte Carlo playoff simulator running 5,000 simulations.
Design study — a concept architecture, not a built system. A data architecture for unifying research and library data: ingestion, a metadata and taxonomy layer, governed search, and an AI assistant layer that answers only with citations.
Good architecture starts at the point of capture. I have sat with the clinicians entering the data, the researchers searching it, and the analysts reporting on it — so I think about data the whole way through: how it is entered, how it is structured and governed, how it is found, and what decision it finally supports. I would rather fix the data model than build a prettier workaround on top of a broken one.