What I'm doing:
As Data Operations Lead, I design, automate, build, and launch scalable, efficient and reliable data pipelines into production using Python.
I've architected and deployed a set of custom Python libraries, including:
an email-parser that processes and applies predictive NLP models to tens of thousands of emails;
a suite of tools that interact with our MPP database (Vertica) to streamline real-time reporting capabilities;
and a tool that generates customizable reports and Excel-based dashboards for our clients.
To support consumer outreach on the Federal Marketplace (Healthcare.gov),
I write advanced queries using Ruby-templated SQL that enable millions of pieces of consumer outreach, per week.
An automated pipeline (built using Python) pulls tagged images of my journey across the US from my email
and maps these images using metadata to the location they were taken in across the US.
This dynamic web app (built using Flask and deployed with Docker on AWS) features an automated web-scrapping pipeline
that tracks gun violence by state and supports a
that sends Tweets about the rate of gun violence across the country and in individual states.
What I've done:
As part of Harvard's CS50: Artifical Intellgience, I implemented an AI to play tic-tac-toe again a user and solve a Minesweeper puzzle.
This simple site was designed in support of the grassroutes campaign for a local elected representative.
A mini-series featuring voices from global experts on the role of strategic purchasing in primary health care improvement.
Case studies from five countries on data innovations in primary health care.
A practical guide to address common measurement challenges faced by countries to support their collection of more useful data on PHC system performance.
You can view the source code for this website, and other projects, on GitHub.