Matt M. Casey
About Matt M. Casey
Matt M. Casey serves as the Data Science Content Lead at Snorkel AI, where he has increased the publication rate by approximately 30% and implemented tools to enhance content quality. With a background in data science and journalism, he has contributed significantly to discussions on AI development and data labeling.
Work at Snorkel AI
Matt M. Casey has been serving as the Data Science Content Lead at Snorkel AI since 2022. In this role, he has increased the overall publication rate by approximately 30%. He has implemented various tools using Python, APIs, and large language models to enhance content quality and reduce delivery time by up to four hours per post. Additionally, he has represented Snorkel AI in discussions with major companies, including AWS, to expand content distribution.
Previous Experience
Before joining Snorkel AI, Matt worked at IQM Corporation as a Data Scientist from 2018 to 2022. He also held the position of Founder and Editorial Director at Clever Move LLC from 2014 to 2016. Earlier in his career, he served as an Editor at Patch.com from 2010 to 2012 and worked as a Ghost Writer at the MIT Center for Transportation & Logistics from 2013 to 2014.
Education and Expertise
Matt M. Casey studied at the University of Massachusetts Amherst, where he earned a Bachelor of Arts in Journalism, History, and English from 2002 to 2006. He furthered his education in data science at Galvanize Inc, completing the Data Science Immersive program in 2017. In 2022, he obtained a certificate in Machine Learning from FourthBrain.
Contributions to AI and Data Science
Matt has contributed significantly to discussions surrounding AI development and data-centric approaches. He emphasizes the importance of high-quality training data and data labeling in the context of large language models. His work includes writing a practical guide on data labeling and analyzing the impact of generative AI across various industries.
Insights on Generative AI
Matt M. Casey provides insights into the challenges and opportunities presented by generative AI. He explores different approaches to data labeling, including manual and programmatic methods, and highlights the critical role of data labeling in the development of AI and machine learning applications.