The People for Shaun Scott (Seattle, WA)

Data Director, May 2019 - November 2019

  • Provided analytical support to the campaign’s organizing and fundraising efforts using Votebuilder, Google Data Studio, and R. Primary responsibilities included precinct targeting, Votebuilder and Hustle administration, and field analysis/organization.

  • Fighting for a Seattle Green New Deal, progressively funded investments in public housing and mass transit, and anti-carceral approaches to helping the most vulnerable members of our community.

Adaptive Biotechnologies (Seattle, WA)

Data Analyst, November 2018 -

  • Provide business intelligence and analysis to stakeholders across the company by managing and designing reporting dashboards, developing KPIs, and helping to ensure the integrity of the data warehouse and data mart using Tableau, SQL, and R.

  • Facilitate use of business data across the company by managing JIRA support requests in an agile development environment and providing user education and training, including office hours for Tableau users.

Nevada State Democratic Party (Las Vegas, NV)

Deputy Data Director, August 2018 - November 2018

  • Used SQL and Google Data Studio to build dashboards that informed daily resource allocation and field operations for the top Democratic Senate campaign of 2018.

  • Regularly performed predictive model assessment, geospatial analysis of voting targets, and other ad hoc analyses in R to inform big picture get-out-the-vote and voter protection strategies.

National Endowment for Democracy (Washington, D.C.)

Stanford Fellow, July 2017 - September 2017

  • Wrote independent desk analysis of the NED’s grant making program in Burma.
  • Reviewed grant materials and interviewed country experts to contextualize the NED’s role in the recent political transition and made recommendations for improved grant making practices in Burma and future transitions.

Taoyuan City Government, Department of Education (Taoyuan, Taiwan)

Data analysis intern, June 2016 - August 2016

  • Used R for data cleaning, visualization, and basic statistical learning methods on data concerning high school and university outcomes in Taoyuan.

Stanford University (Stanford, California)

Research assistant, January 2018 - May 2018

  • Literature review and survey design for Nathan Lee, PhD candidate in political science at Stanford University, in a study of local officials’ perceptions of policy expertise.

Course assistant, June 2017 - March 2018

  • Helped design and organize EDUC 122Q / HISTORY 52Q: American Democracy in Crisis with Prof. Thomas Ehrlich.

Bridge peer counselor, April 2017 - June 2018

  • Volunteered for weekly shifts to provide anonymous counseling for the Stanford community.
  • QPR suicide protocol certified.


Stanford University

  • ENGR 150: Data Challenge Lab (Bill Behrman, Hadley Wickham)
  • STATS 116: Theory of Probability (Rachel Wang)
  • STATS 200: Statistical Inference (Joseph Romano)
  • STATS 204: Sampling (Rajarshi Mukherjee)
  • STATS 216: Statistical Learning (Robert Tibshirani)
  • POLISCI 150A: Data science for politics (Andrew Hall)
  • POLISCI 150B: Machine Learning for Social Scientists (Justin Grimmer)
  • POLISCI 150C: Causal Inference (Jens Hainmueller)
  • POLISCI 358: Data-driven politics (Adam Bonica)
  • HISTORY 4: Introduction to Geospatial Humanities (Zephyr Frank)


  • OSPOXFRD 195Z: Tutorial in political science (Hongping Nie, Oxford University)
  • MTC 7186: Intensive Practical Chinese Reading & Writing (Murong He, National Taiwan Normal University)

Recent Posts

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Back when I was in the DCL, some of my favorite assignments involved recreating graphics from The Upshot at the The New York Times. I was working on some post-primary analysis for the Shaun Scott campaign recently when I recalled a graphic from 2016 that compared county-level election results between the 2016 and 2012 presidential races. I like this style of electoral map because it draws the viewer’s attention to the change taking place on election night, and it indicates which direction the country is headed at a glance.


About a year ago, I started recording my sleep schedule in a Google sheet. Something in the back of my unemployed mind told me that this would be good content for my new website, which I was building mainly in hopes of finding a job – after all, what potential employer wouldn’t want to see the kind of healthy bedtimes I was managing? I didn’t end up using the data because – surprisingly enough – it would be a long time before I had enough to amount to anything interesting.


A draft of my recent work on leaked emails and political knowledge is now available for comment. Below is a copy of the abstract, for reference. In scholarly and popular discourse, leaks have long been associated with transparency, but their potential as a source of disinformation has gone largely unexamined. The turmoil of the 2016 U.S. presidential election cast doubt on leaks’ association with the truth by demonstrating the potentially disorienting impact of leaked emails on political knowledge and discourse.


Before I enrolled in the Data Challenge Lab at Stanford, my approach to data science in R was almost entirely ad hoc. Beyond the very fundamentals of base R, I relied on a combination of Stack Overflow and code copied from textbooks and lecture slides to get anything done. Naturally, I couldn’t do much. The DCL changed all of that by introducing me to the Tidyverse, which not only gave me a unified set of tools to explore, visualize, and customize my data, but also gave me a much more intuitive sense for how data works and feels.


While it’s hard to beat the ease and expressiveness of ggplot2 for most of my visualization needs, I haven’t always been terribly happy with how it’s default settings look. The process of creating a presentation quality plot usually involves toying around with the 80+ arguments of theme() and trying to remember what they control and what kind of element_ they are. It’s easy enough to remember how to change an axis.



Scraping the news

Collecting and tidying headlines from Memeorandum.com

Policing trends in San Francisco

EDA of SF Police Department incident data

Four years on the farm

A quick analysis of my time at Stanford