Talex Diede (She/Her)
About Talex Diede (She/Her)
Talex Diede is an Actuarial Data Scientist with a strong academic background in Applied Mathematics and Economics, as well as Computational Finance and Risk Management. She has over a decade of experience in predictive modeling and data analysis, currently working at Milliman in the Greater Seattle Area.
Work at Milliman
Talex Diede has been employed at Milliman since 2013, serving as an Actuarial Data Scientist. In this role, she is part of the Life and Annuity Predictive Analytics team, where she focuses on predictive modeling to understand policyholder behavior. Her work involves utilizing both traditional statistical model building methods and machine learning techniques. Diede's expertise in data manipulation and analysis contributes to informed business actions within the organization.
Education and Expertise
Talex Diede earned her Bachelor of Science degree in Applied Mathematics and Economics from the University of Washington, studying from 2009 to 2012. She furthered her education by obtaining a Master of Science in Computational Finance & Risk Management from the same institution, completing her studies in 2013. Her academic background provides a strong foundation for her work in actuarial science and data analysis.
Background
Before joining Milliman, Talex Diede worked as an Administrative Assistant at the Port of Everett from 2009 to 2012. This role provided her with valuable experience in administrative tasks while she pursued her studies at the University of Washington. Diede transitioned to Milliman as an Actuarial Intern for three months in 2013, which marked the beginning of her career in actuarial science.
Achievements
Talex Diede specializes in incorporating external data sources to enhance the understanding of customer needs and behaviors. Her contributions to the Life and Annuity Predictive Analytics team have focused on developing predictive models that provide insights into policyholder behavior. Diede's experience in both statistical modeling and machine learning methods positions her as a knowledgeable professional in the field of actuarial data science.