About
I'm a data professional who enjoys solving problems and uncovering insights that drive real impact. With a background in finance, I bring a unique perspective that combines analytical thinking with business context.
I’m someone who enjoys challenging themselves with work projects that allow me to grow my skillset. Some of my recent work includes building a Markov Analysis model to predict the probability of the next month’s charge-off amount, resulting in a margin of error of 2%. Using SQL and Python, I was able to clean and transform the data to capture the historical percentage changes of borrowers’ delinquencies from month to month.
Outside of work, I’m passionate about growing my skillset through competitions and side projects. This past year, I competed in the NFL Big Data Bowl, using real in-game data to create a Random Forest Classifier to predict the probability that the next play would be a run play based on the offense’s pre-snap movement. This achieved an accuracy of 80.9% and a ROC AUC of 87.4% on the test set. I also built a ETL pipeline using Community Transit’s publicly available data, transforming and loading it into structured tables for end user access.
As someone who’s been an end user in the data lifecycle, I understand the value of clean, organized data and the challenges when it’s not. Continuous improvement is a core part of how I work and how I approach life. I regularly attend local data meetups in the Seattle area to connect with others in the field and stay inspired by the projects and ideas people share.
Being a strong teammate is something I take a lot of pride in. I believe that when we work together and lean on each other’s strengths, then we can solve far more than if we were alone. I’m a gritty, reliable team player, and one of my proudest accomplishments is being the first in my family to graduate college while working two jobs to pay for tuition.
