Bio

A modern polymath, John possesses a unique and diverse set of skills, knowledge, and experience. Having earned advanced degrees in mechanical engineering, kinesiology and data science, his expertise focuses on machine learning, and solutions to novel and ambiguous problems. He has a proven history of taking a problem from ideation to production by using a logical, but creative, data-driven approach. As a highly skilled Data Scientist, he has developed new techniques, led teams, developed innovative data products and is a trusted advisor to decision-makers. John is a natural leader, customer-focused, an excellent communicator, and a problem-solver. He loves new challenges and opportunities. His extensive background in software development and modelling serves him well. His curiosity, creativity, focus and attention to detail have resulted in a track record of discovering hidden secrets in data. He has founded and worked with data in start-ups where there was limited infrastructure and relatively small data sizes to petabytes of data at FAANG companies with complex infrastructure and data governance rules. Each time he demonstrated his ability to derive meaningful insights from the data and create powerful models that impact customers' lives. John fosters the growth of scientists by starting the Amazon Machine Learning University in Irvine and the Alexa-wide Data Science Excellence program. He frequently gives talks at universities and conferences. John is a co-organizer of the largest R meetup group in Southern California (SoCal RUG). The group explores data science, data analysis, visualization, data mining, predictive analytics, and beyond with a focus on R. They hold monthly meetups, book clubs, training courses, data science hackathons, and much more. He is working to improve upon and formalize data science best practices. The focus has been on reproducible research. To that end, he has developed an approach to improve data validation and reliability by using data unit tests and techniques for managing feature types. He has also developed the Data Science Design Thinking concept; to formalize and increase the efficiency of the analysis process. For decades, one of his passions has been mentoring people. Especially helping women advance their careers in technology. He is doing this because of a strong belief that women should have a seat at the tech table. He has helped many people to successfully transition from academia to industry. These include undergrads, grad students, post-docs, project scientists and even faculty members.