About

Britton Winterrose is a Startup Solutions Architect at Amazon Web Services (AWS) with a diverse background in entrepreneurship, machine learning, and software engineering. With over 9 years of relevant experience, Britton has a record of success in helping businesses break through performance barriers and grow. Britton is a 2x founder turned Machine Learning Engineer, intensely curious about data science, python software engineering, machine learning, artificial intelligence, presenting, and Kaggle competitions. He is a passionate software engineer, relentless full-stack data scientist, and accomplished entrepreneur. His current role at Amazon involves working with top SF-based accelerator/incubator firms and their portfolio companies to drive technology behind their key business outcomes on AWS. He is helping startups succeed by building and deploying data science products at scale. Prior to his current role, Britton founded a mid-scale Bitcoin mining operation called Darksaber, raised $115,000 in funding, and built the company from scratch. He managed all operations and growth and eventually sold the company in a private acquisition in October 2018. Britton's education background includes a Data Science Program in Machine Learning and Artificial Intelligence from Lambda School and a degree in Business Management with a focus on Supply Chain and Operations Management from Brigham Young University - Hawaii. His tech stack includes AWS, senior software engineering, ML, web, data scientist, python, SQL, software engineering, Bitcoin, NLP, Spark, recommendation systems, product management, Keras, Seaborn, data engineering, research scientist, deep learning, Scikit, test, Docker, TensorFlow, Pandas, neural networks, ETL, Flask, backend, and Scala. Britton's notable accomplishments include winning 1st place and $18,000 in the Pioneer App + Lambda School Challenge in May 2019, placing 11th in the Kaggle Days SF 2019 Hackathon against multiple Kaggle Masters and Grandmasters, and building an eight-person hybrid team and making a full-stack app with an enterprise scalable machine learning backend in five weeks for Larkist.