Daniel J. Matthews, Ph.D.
About Daniel J. Matthews, Ph.D.
Daniel J. Matthews, Ph.D., is a Data Architect at Confluence with a strong academic background in Physics and Astronomy. He holds a Ph.D. from the University of Pittsburgh and has experience as an Assistant Professor and various roles in data analysis.
Work at Confluence
Daniel J. Matthews has been employed at Confluence since 2019, serving as a Data Architect. In this role, he has applied his expertise in data modeling and optimization techniques. Prior to his current position, he worked as a Senior Regulatory Reporting Data Analyst for 11 months and as a Regulatory Reporting Data Analyst for two years. His contributions have focused on enhancing data processes and ensuring compliance within the Greater Pittsburgh Area.
Education and Expertise
Daniel J. Matthews holds a Doctor of Philosophy (Ph.D.) in Physics from the University of Pittsburgh, which he completed between 2007 and 2014. He also earned a Master of Science (MS) in Physics from the same institution from 2006 to 2007. Prior to that, he obtained two Bachelor of Science (BS) degrees, one in Physics and another in Astronomy, from the University of Iowa from 2002 to 2005. His academic background provides a strong foundation for his skills in data analysis and modeling.
Background
Before joining Confluence, Daniel J. Matthews worked at Chatham University as an Assistant Professor of Physics from 2014 to 2016. He also served as a Graduate Student Researcher at the University of Pittsburgh for seven years, where he developed his research skills and expertise in data analysis. His career trajectory reflects a commitment to both academia and industry, particularly in the field of data architecture.
Technical Skills and Proficiencies
Daniel J. Matthews possesses a range of technical skills relevant to data architecture and analysis. He is skilled in optimizing models using tuning parameters and regularization techniques. He is proficient in validating results through data splitting and A/B testing. Additionally, he has expertise in various forms of regression analysis and in calculating loss functions to minimize risk, which are critical skills in data-driven decision-making.