Led a team of six data analysts to successfully execute a strategy to win elections for Joe Biden and John Hickenlooper in the 2020 presidential and US Senate elections in Colorado.

- Built Michigan's GOTV Percent to Goal (PTG) report in Google Sheets.- Built a program that emailed organizers every day with data on their progress to goal for GOTV shifts recruited and a list of their unclosed shifts. The system also sent region-level summary emails to RODs.- Wrote SQL to pull lists for email and texting mobilization, and Python scripts for retrieving nearest event.- Wrote Google Apps script in JavaScript for sending email notification to data team and other senior staff when Google form was submitted for list request.

- Created reports in Tableau, helped to administer VAN, answered data tickets, trained organizers on VAN.- Built several reports for the IA relational organizing program and volunteer pipeline in D3.js on Civis Platform and Iowa’s GOTC soft report in Google Sheets based on data submitted by every staging location director after every shift.- Built detailed volunteer tracking Google Sheet reports for every region that updated automatically with both summaries and volunteer lists.- Built a workflow for automatically creating a Google Sheets turf tracker for 120 staging locations. The workflow updated the prioritization of each precinct, allowing senior staff to re-prioritize precincts during GOTC.- Engineered multiple data pipelines to automate pushing data from MobilizeAmerica to VAN and Civis Platform using Mobilize, VAN, and Civis APIs.

- Engineered client dashboard web application using AngularJS, Express, and Vega, a D3.js-based library.- Engineered a new, faster data pipeline for a client, primarily in Python and Pandas. Implemented new data definitions and wrote explanations for a non-technical audience and technical documentation for the client tech team. - Automated the writing and running of standard SQL queries using the Civis Analytics API in Python.- Scripted election night reporting programs in Python for key races, scraping data from state websites.- Supported clients with VAN administration.

CS171 Data Visualization teaching staff for Fall 2017: CS171 teaches students to tell a story around a dataset, mainly using the D3.js library (version 4). My responsibilities included grading and holding office hours for a total of about 5 hours per week.

Took a semester off from Harvard to intern in the data analytics department in the Brooklyn HQ for two months, then was deployed to state data team in Raleigh, North Carolina for the last 2.5 weeks of the campaign. In Brooklyn, I was in the Organizing subteam of Analytics, which dealt with field and volunteer data. Projects I worked on include:- Audited an algorithm that determined the 'walkability' of GOTV targets, including writing a memo for supervisors on my results.- Wrote a Python program to clean a dataset of names into first names, last names, middle names, prefixes and suffixes.- Wrote a Python wrapper to pull JSON data from the RealClearPolitics polling average API, and open-sourced it on GitHub.- Wrote a web scraper in Python to get the correct polling location addresses in North Carolina from the state Board of Elections website, which allowed us to find and correct ~230 wrong addresses in our database for use on the Hillary Clinton website in time for Election Day.

Helped professors put on the inaugural Political Analytics Conference on April 1, 2016. My main responsibilities were contacting journalists (including drafting and sending out media advisory) and political professionals, and handling guest registration for several hundred attendees.