About

David Berger is a highly experienced Staff Machine Learning Engineer with a passion for using machine learning to build data-driven products that connect customers with what they love. With over 9 years of relevant experience, David has a proven track record of building and implementing runtime personalization models using neural nets and reinforcement learning. Currently working at Grubhub, David is responsible for building and deploying models to drive customer growth. He serves personalized product recommendations with Deep Learning recommender models and de-duplicates product catalogues with bespoke NLP models. David is also building out Grubhub's ML Platform. David has previously worked as a Data Scientist and Full Stack Developer at bookbrew.io book recommender, where he built an interactive book recommender web app called BookBrew. He created and managed RDS database using PostgreSQL, implemented AWS Elastic Beanstalk development/deployment pipeline for Flask-based app, and built a recommendation engine combining collaborative filtering built on matrix factorization and content-based filtering built on keyword clustering and topic modeling. David also designed an innovative recommendation interface whereby users mix and match different book and feature combinations to find their ideal recommendation. He created a D3.js visualization to depict topic importances of any book selected. David has also worked at Metis as a D3.js Data Visualization expert, where he helped teach intensive onsite instruction and immersive data visualization course using D3.js. David holds a Bachelor's degree in Political Science from Yeshiva University, where he majored in Political Science. He is highly skilled in ML, Reinforcement Learning, Deep Learning, NLP, Javascript, AWS, Data Scientist, Research Scientist, Flask, and Web. David is a highly motivated and dedicated individual with a passion for using machine learning to build data-driven products.