About

Samuel Taylor is a Senior Machine Learning Engineer at Coinbase, based in Austin, TX. With 6.78 years of relevant experience, he has a strong background in computer science and has worked for companies such as TripAdvisor, Indeed, and Acrisure. Samuel is passionate about helping scientists and researchers make real business impact through good engineering. He believes that relationships matter more than org charts, learning happens best through intelligent action, and that teamwork is leadership. Samuel is proud of the students he has helped learn to code, his pun generator, and a talk he delivered at one of the largest data science conferences. Samuel's interests include trustworthy models, imbalanced data, and putting machine learning into production. At Coinbase, he designed and implemented a machine learning system to rank leads for a sales team using logistic regression and GBDT models on Kubeflow. He also used unsupervised learning techniques to recommend new prospective business customers to insurance agents and created a distributed training pipeline using Kubeflow and Spotify Annoy. In his past roles, Samuel has designed A/B tests and built, trained, and monitored machine learning models used in a large-scale production system. He has also written maintainable, scalable, production-level Java code used to serve large volumes of traffic. Samuel's tech stack includes ML, Scikit, Web3, test, Kubernetes, Tensorflow, Data Scientist, Research Scientist, Spark, Scala, Java, and Apache Hive. Samuel holds a Bachelor of Science in Computer Science from Baylor University. He is a dedicated professional who is passionate about using his skills to make a real impact in the world of machine learning.