About

Allie Cailer is a highly experienced Machine Learning Engineer currently working at Spotify, where she is responsible for developing and implementing personalized playlists. With over 7 years of relevant experience, Allie has a proven track record of building, deploying, and evaluating production machine learning models, data pipelines, and data collection systems for personalization in online learning and content discovery. Prior to joining Spotify, Allie worked as a Senior Machine Learning Data Scientist at Coursera, where she was involved in projects related to search, deep learning recommenders, and NLP-based content metadata tagging. She was promoted to Senior in March 2021, a testament to her dedication and expertise in the field. Allie also worked as a Machine Learning Data Scientist at Affirm, Inc., where she and her team created, maintained, and improved the core machine learning technology that powers algorithmic decisioning. She specifically worked on the underwriting model and consumer pricing, demonstrating her versatility and ability to adapt to different projects and challenges. Allie holds a Bachelor's degree in Economics, Mathematical Track from Princeton University, a Master's degree in Computer Science from the University of Illinois, and is currently pursuing a certificate in Climate Change: Learning for Action from Terra.do. Her technical skills include ML, Data Scientist, Research Scientist, Deep Learning, NLP, and Search, making her a well-rounded and versatile professional. In summary, Allie Cailer is a highly skilled and experienced Machine Learning Engineer with a proven track record of success in developing and implementing personalized playlists and other projects related to machine learning. Her dedication, expertise, and versatility make her a valuable asset to any organization.