Angella K.
About Angella K.
Angella K. is a Senior Actuarial Assistant at Mutual of Omaha, where she supports annuity experience studies and contributes to product team data support. She has a background in mathematics and data science, with research interests in Chaos Theory and bias-variance reduction in big data.
Current Role at Mutual of Omaha
Angella K. currently serves as a Senior Actuarial Assistant at Mutual of Omaha, a position she has held since 2021. In this role, she supports annuity experience studies and external data reporting. She contributes to the product team data support, focusing on monitoring product performance for traditional whole life insurance products. Her work is conducted remotely, allowing her to engage with various teams and projects effectively.
Previous Experience at Mutual of Omaha
Prior to her current role, Angella K. worked at Mutual of Omaha as an Actuarial Assistant for six months in 2020. She also completed a three-month internship as an Actuarial Intern in 2019. During her time at the company, she gained valuable experience in actuarial practices and contributed to various projects related to data analysis and reporting.
Educational Background
Angella K. holds a Master of Science degree in Data Science from the University of Nebraska at Omaha, which she is expected to complete in 2024. She also earned a Master of Science in Applied Mathematics from California State University, Northridge, from 2018 to 2020. Additionally, she obtained a Bachelor of Science in Mathematics from the University of Hartford, graduating in 2016.
Teaching and Academic Contributions
Angella K. has experience in academia, having served as an Adjunct Lecturer at California State University, Northridge in 2021 for four months. She also worked as Adjunct Faculty at Casa Loma College for five months in 2021-2022. Her roles included teaching and assisting in mathematics courses, contributing to the education of students in her field.
Research Interests and Expertise
Angella K. has a strong interest in Chaos Theory and engages in research focused on bias-variance reduction and trade-off in big data. She specializes in computer vision and pattern recognition, as well as partial differential equations, particularly Fourier Transforms and Green's function. Her academic and research pursuits reflect her commitment to advancing knowledge in these areas.