About Me
I’ve heard it said that data science is storytelling with numbers. You start with a bunch of numbers (data), find the patterns, answer important questions, and effect meaningful change.
When I was 5 years old, I learned about square numbers. I noticed that the differences between successive square numbers followed a pattern. 1, 4, 9, 16, 25, etc. The gaps were 3, 5, 7, 9 — odd numbers. And so I took a sheet of paper and wrote out the first 50 square numbers by hand using my newfound formula. Four-year-old me couldn’t tell you what 47 squared was. Now I knew, and I was eager to share my knowledge with all my kindergarten friends. Real numbers, real patterns, real answers, real effects.
When I was 7, I was introduced to Microsoft Excel, and I immediately fell in love. I finally had an easy way to organize all of my numbers! Some of the first spreadsheets I worked with involved Pokémon base stats. Which Pokémon should I use for my video game playthrough? Which Pokémon has the highest Attack stat while also having a Defense stat higher than 100? I could answer these questions and more using formulas and pivot tables.
When I was 9, I started closely following NBA basketball. I didn’t want to just watch the games — I wanted to do something with the numbers. And so I started developing a simple basketball simulator using dice. Over the course of the next decade, I refined my simulation, testing and adjusting with the goal of making it as realistic as possible. Eventually I implemented it in Python code. If the Oklahoma City Thunder had made the 2026 NBA Finals, would they have beaten the New York Knicks? My simulator has them winning in a tight 7-game series. Real numbers, real patterns, real answers, real effects.
When I was 16, I graduated high school, two years early. I decided to take a gap year to hone my skills and knowledge as a data scientist. I took online courses and grew my understanding of the field. My biggest takeaway? Data science is fundamentally what I had been doing all along: taking real numbers, finding real patterns, answering real questions, and effecting real change.
Education
University of California, Los Angeles
B.S. Statistics & Data Science — 3.6 GPA
Relevant coursework: Linear Models, Monte Carlo Methods, Statistics in Finance, Geostatistics, Causality
Actuarial Exams
Experience
Data Detectives • Remote
Data Analytics Intern
- Built automated data pipelines in Python to clean and transform Bay Area housing datasets for predictive modeling
- Generated synthetic training datasets using the SDV library to augment model performance
- Key lesson: data is everywhere — models are critical to help us draw insights and make decisions from it
Awe & Reverence • Remote
Software Apprentice
- Built full-stack websites, gaining experience with React, MySQL, and other common tools
- Key lesson: good communication and solid coding practices are the cornerstones of the tech world
Rooted Software • Concord, CA (Hybrid)
Information Technology Intern
- Developed mobile applications for a church with 1,000+ members
- Configured hardware for a private K–8 school with 400+ students
- Key lesson: skills are most valuable when used to effect real, meaningful change
Chick-fil-A • Walnut Creek, CA
Front of House
- Took and fulfilled orders for 100+ guests per shift
- Demonstrated leadership and communication in a professional environment
- Key lesson: high agency and dedicated drive can make a big difference in any working environment