About
A data analyst with a public-health background.
Here's a bit more about my background and what I'm doing now.
Background
I studied global public health at NYU and went on to the Simon Lab at Mount Sinai, which ran antibody studies across several NIH-funded cohorts. I started there as a clinical research assistant in 2022 and moved into a data analyst role in 2023.
Over two and a half years I built sample-tracking systems for biospecimens, reviewed data quality with the clinical and lab teams, and analyzed longitudinal serology data in Python and SAS. That work went into figures and statistical summaries for published papers, including two where I'm a named co-author.
Now I'm finishing an M.S. in Data Science at Pace and building data projects of my own. The biggest is ViewerAtlas, which samples Twitch chat three times a day and maps which channels share an audience. I always try to keep in mind what any data actually represents and state plainly what can't be shown in my results.
- Location
- New York City
- Current focus
- M.S. Data Science, Pace University
- Prior role
- Data analyst, Mount Sinai · Simon Lab
- Core tools
- Python · SQL · SAS · R
- Publications
- 9 papers, named co-author on 2
- Highlighted project
- ViewerAtlas case study
How I work
Most of these habits come from working in a research lab, where a mistake in the data can end up in a published paper.
Check the data first
Before any analysis, I want to know where the data came from, what's missing, and what shouldn't go into a model at all. Sorting that out early makes the results easier to reproduce and explain.
Show the work
I've made figures and statistical summaries for papers in journals like Immunity and Cell Reports Medicine. I try to lay out results so someone else can check them, including where the data falls short.
Work across teams
At Mount Sinai I coordinated with clinical, laboratory, and data teams on the same studies. I'm comfortable moving between analysis, documentation, and dashboards, depending on what a project needs.
Implementing AI in Workflows
I write code in Python, TypeScript, and Rust, and I use AI coding assistants regularly for implementation, debugging, and documentation. I try to use AI to improve my outputs and make both life and work easier.