Joseph Tenini, PhD
About Joseph Tenini, PhD
Joseph Tenini, PhD, serves as the Principal Data Scientist at Red Ventures, where he has worked since 2021. He holds a PhD in Mathematics from The University of Georgia and has extensive experience in data science, including roles in academia and various companies.
Work at Red Ventures
Joseph Tenini has been serving as Principal Data Scientist at Red Ventures since 2021. In this role, he has directed end-to-end data science projects that integrate business and technology aspects. Prior to his current position, he worked as a Senior Data Scientist at Red Ventures from 2019 to 2021 and as a Data Scientist from 2017 to 2019, both based in Fort Mill, South Carolina. His work at Red Ventures emphasizes the importance of collaboration with business and technology stakeholders to ensure the success of data science initiatives.
Education and Expertise
Joseph Tenini earned his Doctor of Philosophy (PhD) in Mathematics from The University of Georgia, where he studied from 2012 to 2014. He also holds a Master of Arts (MA) in Mathematics from the same institution, completed from 2009 to 2012. Earlier, he obtained a Bachelor of Science (BS) in Mathematics from Furman University, graduating in 2009. His academic background supports his specialization in implementing contextual multi-armed bandit policies and recommender systems for real-time user treatment personalization.
Professional Background
Joseph Tenini's professional experience includes various roles in data science and education. He worked at Epic as a Business Intelligence Developer/Data Scientist from 2015 to 2017 and as a Technical Trainer from 2014 to 2015, both in Verona, Wisconsin. Additionally, he served as a Graduate Teaching Assistant at The University of Georgia from 2009 to 2014. His early career also included research positions at the University of West Georgia and Lafayette College in 2007 and 2008, respectively.
Achievements in Data Science
Throughout his career, Joseph Tenini has executed online learning algorithms that operate in the sub 100ms range. He believes in the importance of deep integration with business and technology stakeholders for successful data science projects. His expertise in data science is reflected in his ability to implement advanced techniques, such as contextual multi-armed bandit policies and recommender systems, which enhance user experience through personalized treatment.