Cheng Chang
About Cheng Chang
Cheng Chang is a Senior Developer at Transamerica, specializing in ALM Modeling. He has a strong educational background with master's degrees in Computer Science and Applied Statistics, and has held various analytical roles within the company since 2015.
Work at Transamerica
Cheng Chang currently holds the position of Senior Developer in ALM Modeling at Transamerica, a role he has occupied since 2019 in Cedar Rapids, Iowa. His responsibilities include supporting the modeling of external projected assets in MoSes CFT models for traditional life and universal life products. Prior to this position, he worked as a Senior Analytical Reporting Analyst in Enterprise ALM from 2018 to 2019, and as an Intermediate Risk Analyst leading the User Acceptance Test Team from 2016 to 2018. Cheng also served as an Actuarial Clerk for IFRS Reporting for VA Business from 2015 to 2016.
Education and Expertise
Cheng Chang has a strong educational background in quantitative fields. He earned a Master's degree in Computer Science from the Georgia Institute of Technology, studying from 2021 to 2023. Prior to this, he completed a Master's degree in Applied Statistics at DePaul University from 2013 to 2015. Cheng also holds a Bachelor of Science degree in Applied Mathematics and Physics from the Illinois Institute of Technology, where he studied from 2008 to 2013. His education equips him with the skills necessary for his roles in analytical reporting and modeling.
Background
Cheng Chang's professional journey began with a role as an Actuarial Clerk at Transamerica in 2015. He transitioned to various analytical positions within the company, gaining experience in risk analysis and reporting. His tenure at Transamerica has spanned multiple roles, contributing to his expertise in asset modeling and risk management. Cheng's career reflects a consistent focus on analytical and technical skills within the financial services sector.
Achievements
At Transamerica, Cheng Chang leads a Python-based ALM model improvement project that emphasizes variable vectorization and the redesign of code structure. This initiative aims to enhance the efficiency and performance of ALM models. His contributions to the modeling of external projected assets in MoSes CFT models further demonstrate his commitment to advancing the company's analytical capabilities.