Moshe Ivry
About Moshe Ivry
Moshe Ivry is an Algorithm Team Leader at Earnix, where he has focused on advanced optimization techniques for nearly a decade. He has a background in software development and has worked at several companies, including Deutsche Telekom Innovation Labs, Amdocs, and Woobi.
Work at Earnix
Moshe Ivry has been serving as the Algorithm Team Leader at Earnix since 2015. He focuses on advanced optimization techniques and leads a team dedicated to developing innovative algorithms. His work involves designing and implementing optimization algorithms for regression modeling, utilizing methods such as Generalized Linear Models (GLM), Generalized Additive Models (GAM), Newton-Raphson, and BFGS. His leadership contributes to the company's efforts in enhancing algorithmic solutions in the financial technology sector.
Previous Experience in Software Development
Before joining Earnix, Moshe Ivry held several positions in software development. He worked at Deutsche Telekom Innovation Labs @ Bgu as a Software Developer from 2007 to 2009. He then transitioned to Amdocs, where he served as a Software Developer for one year in Chesterfield, Missouri, from 2013 to 2014. Additionally, he worked as a Senior Software Developer at Woobi for seven months in 2014. These roles provided him with a solid foundation in software engineering and algorithm development.
Education and Expertise
Moshe Ivry studied at The Open University of Israel from 1998 to 2003, completing a degree that laid the groundwork for his career in technology. He furthered his academic pursuits at Ben Gurion University of the Negev, where he was a PhD student and Teaching Assistant from 2010 to 2012. His educational background supports his specialization in designing and implementing optimization algorithms, particularly in regression modeling.
Research and Academic Involvement
During his time at Ben Gurion University of the Negev, Moshe Ivry engaged in research as a PhD student and served as a Teaching Assistant. This role allowed him to contribute to academic projects and support students in their learning. His involvement in academia has influenced his approach to algorithm development and optimization techniques.