Daniel Sikes

Risk Modeler Ii @ Credit One Bank

About Daniel Sikes

Daniel Sikes is a Risk Modeler II at Credit One Bank, specializing in optics and analytical modeling. He has a PhD in Physics from the University of Wisconsin-Madison and has authored several publications in computational physics and applied mathematics.

Work at Credit One Bank

Daniel Sikes has been employed at Credit One Bank as a Risk Modeler II since 2022. In this role, he utilizes his expertise in risk modeling to analyze and mitigate potential financial risks. His strong background in optics and analytical modeling supports his contributions to the bank's risk assessment strategies.

Education and Expertise

Daniel Sikes holds a Bachelor of Science in Physics and a Bachelor of Science in Applied Mathematics from North Carolina State University, where he studied from 2002 to 2006. He further advanced his education by earning a PhD in Physics from the University of Wisconsin-Madison, completing his studies from 2006 to 2012. His academic background provides a solid foundation for his work in computational physics and applied mathematics.

Background

Prior to his current position, Daniel Sikes served as an Adjunct Instructor at Portland Community College from 2013 to 2022. During his nine years in this role, he taught various courses, sharing his knowledge in physics and mathematics with students in the Portland, Oregon area. His teaching experience complements his technical skills in risk modeling.

Publications and Research

Daniel Sikes has authored several publications in leading journals, reflecting his expertise in computational physics and applied mathematics. His research contributions demonstrate his ability to tackle complex scientific problems through analytical modeling and data analysis.

Technical Skills

Daniel Sikes possesses a robust background in computer programming, which enhances his capabilities in data analysis and risk modeling. His technical skills are integral to his role in assessing financial risks and developing effective modeling strategies.

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