Jose Fuentes

United States of America

About Me

For a long time, the honest answer to “what do you want to do?” was “not sure yet.” That changed when a game design class and CS principles course clicked—building things felt natural, and debugging felt like solving good puzzles. Since then, the throughline has been simple: ship useful systems, learn fast, and let results do the talking. Lately that’s looked like a mix of machine learning and backend work. At UCSF Health, tuned LLMs in Python/Hugging Face to flag lung cancer readmission risk with around 80% accuracy, giving clinicians a head start and cutting manual review time. At MLB, moved venue configurations into Firestore and built an admin flow so 50+ non-technical teammates could update data without redeploys—turnaround went from hours to minutes, and credentials are handled safely. On the sports analytics side, built Python pipelines and PCA/K-means workflows for Bay FC to analyze tracking data—helped staff evaluate player movement patterns and save scouting hours. Earlier at Target, compressed 10+ manual promo tasks into two Kotlin/Kafka endpoints that automated workflows across stores and saved a lot of repetitive effort. Different contexts, same goal: make hard things feel simpler and faster in production. I just finished an M.S. in Data Science at USF; now building toward the intersection of applied ML and sports analytics with projects that are measurable, explainable, and production-minded.

Overview