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Author Profile

John Peach

Principal Data Scientist

  • 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, 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, lead teams, developing innovative data products and is a trusted advisor to decision-makers.

    John is a natural leader, customer-focused, excellent communicator and 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.

    As a Sr. Applied Data Scientist at Amazon, John lead the Alexa Skill Store Science team. He worked closely with engineering to build systems that enabled Alexa customers to engage with third-party applications, skills. He built machine learning models to arbitrate between skills, entity resolution, search, and personalization.

    Currently, John is a Principal Data Scientist at Oracle. He works on the Data Science service as part of the Oracle Cloud Infrastructure team. Leveraging his extensive hands-on experience building machine learning models, he is now defining the tooling to improve the data science workflow. This interest grew out of the challenges that he and his team members have faced working with data at scale in a logical, rigorous and reproducible way.

    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. 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. He has also developed the Data Science Design Thinking concept; to formalize and increase the efficiency of the analysis process. He also coordinates the largest R meetup group in Southern California (OCRUG).

    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, 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, lead teams, developing innovative data products and is a trusted advisor to decision-makers.

    John is a natural leader, customer-focused, excellent communicator and 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.

    As a Sr. Applied Data Scientist at Amazon, John lead the Alexa Skill Store Science team. He worked closely with engineering to build systems that enabled Alexa customers to engage with third-party applications, skills. He built machine learning models to arbitrate between skills, entity resolution, search, and personalization.

    Currently, John is a Principal Data Scientist at Oracle. He works on the Data Science service as part of the Oracle Cloud Infrastructure team. Leveraging his extensive hands-on experience building machine learning models, he is now defining the tooling to improve the data science workflow. This interest grew out of the challenges that he and his team members have faced working with data at scale in a logical, rigorous and reproducible way.

    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. 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. He has also developed the Data Science Design Thinking concept; to formalize and increase the efficiency of the analysis process. He also coordinates the largest R meetup group in Southern California (OCRUG).

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