Rachel graduated from Princeton University in 2013 with a degree in Mathematics. As a mortgage-backed securities trader at Morgan Stanley, Rachel became fascinated with extracting meaning from data through machine learning and visualization. At the NYC Data Science Academy, she built a model that detects Wikipedia articles that fail to maintain a neutral point of view with 90% accuracy using natural language processing and classification techniques. She is dedicated to producing efficient, reusable code as quickly as possible. In addition to her technical skills, Rachel is voraciously curious and a creative thinker who would make a valuable addition to any data science team.