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

2024 – 2026

Actuarial Exams

SOA Probability (P) ExamJan 2026
SOA Financial Mathematics (FM) ExamSitting
Aug 2026

Experience

Data Detectives Remote

Data Analytics Intern

Jun 2025 – Aug 2025
  • 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

Jun 2023 – Aug 2023
  • 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

May 2022 – Aug 2022
  • 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

Mar 2022 – Aug 2022
  • 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

Skills

Industry
AI FluencyData AnalysisProbability TheoryMicrosoft ExcelRPythonSQL
Interpersonal
LeadershipCommunicationAdministration