About Me

Background

I graduated Magna Cum Laude with High Honors from Harvard, double concentrating in Statistics and Economics. My time there shaped how I think about using data to understand human behavior and solve real problems—whether that's predicting elections, understanding what drives economic mobility, or figuring out why people do (or don't) act on their values.

At Harvard, I worked as a course assistant for Raj Chetty's "Using Big Data to Solve Social Problems," explored the gap between beliefs and behavior on climate change with game theorist Erez Yoeli, and studied the effects of discretionary government spending on Mexican electoral outcomes with Professor Nair at the Kennedy School.

That thread—using rigorous quantitative methods to understand behavior and inform decisions—runs through everything I've done since. I've built causal inference pipelines on Walmart's Economist Team under Professor John List, researched banking surveys at the Federal Reserve Bank of Dallas, and analyzed fiscal policy at the Committee for a Responsible Federal Budget.

Current Role

Now, I'm a data scientist at Tesla working on demand and capacity forecasting. I'm passionate about the environment and believe deeply in leveraging technology to accelerate sustainable abundance for all.

What I'm Working On

Currently, I'm building a statistical model to forecast the 2026 U.S. midterm elections, combining polling data, economic indicators, and historical trends. This project builds on my 2024 presidential election forecasting work and represents an ongoing exploration of political data science and probabilistic modeling.

Outside of Work

When I'm not working with data, you can find me running, at CrossFit, or hiking. I'm a huge music fan and am probably reading a Pitchfork album review right now. I also love audiobooks, writing, and trying out new restaurants—send me your favorite Mexican spots in San Francisco!

Find Me Elsewhere

Contact

Email me at grantwilliamsaz [at] gmail [dot] com

Send me any interesting economics or statistics papers you come across! Bonus points if they have to do with politics.