Chris Prosser
About Chris Prosser
Chris Prosser is a Principal Data Science Engineer at Adobe, where he has held various roles since 1997, including Group Data Science Manager and Senior Data Science Engineer. He specializes in integrating business decision-making with technical expertise in statistics and big data analysis.
Work at Adobe
Chris Prosser has held multiple roles at Adobe since 1997. He began as a Computer Scientist on Adobe ImageStyler 1.0 and progressed through various positions, including Engineering Manager for Adobe After Effects and Senior Data Engineer. He served as a Group Data Science Manager from 2016 to 2020, where he initiated the data science group in his division, focusing on causal inference from observational data. Currently, he works as a Principal Data Science Engineer, a position he has held since 2023. Throughout his tenure, he has contributed to projects involving big data analysis and client-side development.
Education and Expertise
Chris Prosser studied Chemistry at Connecticut College from 1991 to 1993 and later pursued Computer Science at the University of Massachusetts Amherst, where he earned a B.S. from 1994 to 1997. His educational background supports his expertise in statistics, big data analysis, and software development. He has applied this knowledge in various technical roles at Adobe, synthesizing business decision-making with technical skills.
Background
Before joining Adobe, Chris Prosser worked as a Software Engineer at Media 100 from 1993 to 1997. His career at Adobe spans over two decades, during which he has taken on roles with increasing responsibility, including multiple senior management positions. He has experience in both engineering and data science, which has shaped his approach to integrating technical solutions with business needs.
Achievements
Chris Prosser has made significant contributions to Adobe's data science initiatives. He built the continuous integration and deployment pipeline used with Databricks and has negotiated with technology partners to ensure the availability of appropriate tools for data science projects. He leads a mixed team of data analysts, data scientists, and data engineers, focusing on optimal tool usage across the organization.