I am currently the Data Director of the Sunrise Movement where I leverage data to build the power to pass a Green New Deal. I work alongside organizers to create novel programs with ambitious yet measurable outcomes, manage a team of data scientists to build the infrastructure and organizational culture to collect data, and analyze the data to uncover the key strategies that build power. I also lead the field research and experimentation for the movement.
I was previously both the head of Automatic Voter Registration and Fundraising at Data for Progress, the Development Manager for New Era Colorado, and the Executive Director of Engineers for a Sustainable World.
>> Data Skills
Data analytics Visualization Machine learning
A/B testing Field Experimentation Causal inference Research
>> Tech Skills
Python SQL R EveryAction Action Network Civis VAN Periscope
I currently lead the strategy for how data is used at Sunrise Movement to build the power to pass a Green New Deal.
Director of Automatic Voter Registration & Fundraising
Data for Progress
Led the organization's research on automatic voter registration and leading their fundraising operations. I was the first person in the country to gain access to the raw AVR data from the Secretary of State offices, and produced a novel method of comparing state reported AVR opt-out rates. I also worked on a short-term grant to research the jobs created by the Green New Deal and the demographics of the people who would have those jobs, focusing on race, gender, age, and geography.
Board Member & Project Manager
Co-led MiDataLabs, a for-profit company that provides data and programming expertise to non profits. I was responsible for project managing the team and leading our sales operations.
New Era Colorado
Lead fundraiser for a $2 million organization that engages young people in democracy to build a better Colorado.
Engineers for a Sustainable World
Executive Director of an international sustainability nonprofit with over 50 collegiate chapters and 2,000 members. At ESW I created and then launched two national programs to build community climate resilience, raised more money for our annual conference than ever before in the organization's 14-year history, and impacted several dozen communities across the world.
Diversity of jobs created by the Green New Deal
Won a grant from Data for Progress to research the diversity of jobs created by the Green New Deal: who gets these jobs, what do they look like, and where do they live?
Neural Net to Identify Rooftop Area for Solar Policy
Independent final capstone of a convolutional neural net to classify rooftop area in Denver.
Built a data generator to apply random cropping and rotations to 100+ hand labeled data.
Constructed and trained a convolutional neural net architecture based off the U-NET architecture.
Ran the trained model on all 1,800+ satellite images for Denver, CO to determine the total estimated rooftop area in the city, then determined that full solar coverage of all rooftops in Denver could not meet Denver’s residential electricity needs.
Springboard Data Science Fellowship
Completed curriculum including Python for Data Science, Data Wrangling, Data Storytelling, Inferential Statistics, and Machine Learning.
Used linear regression with sklearn to determine features that best predict Boston housing prices.
Performed three exploratory data analyses using Python that included techniques such as hypothesis testing, one-sided t and z tests, and bootstrapping.
Investigated a drop in Yammer user engagement data using SQL and concluded that a broken link in an email was the culprit to the dip.
Predicting Gentrification in Denver, CO
Independent capstone for Springboard that included data wrangling, statistical analysis, and machine learning.
Downloaded and cleaned data from both the U.S. Census and data queried from Yelp’s API.
Visualized the relationship between point data of new restaurants and cafes and spatial data of gentrified census tracts in ArcGIS.
Applied inferential statistics to determine if there was a significance between the number of new cafes and restaurants in a census tract and whether that tract had gentrified.