Sikai Wang
About Sikai Wang
Sikai Wang is a Machine Learning Engineer at Quantcast, where he has worked since 2022. He has a strong academic background in applied mathematics and statistics, complemented by extensive experience in machine learning roles across various organizations.
Work at Quantcast
Sikai Wang has been employed at Quantcast as a Machine Learning Engineer since 2022. In this role, he focuses on developing and implementing machine learning models to enhance data-driven decision-making processes. His contributions are integral to the company's efforts in leveraging data analytics for advertising solutions.
Previous Experience in Machine Learning
Prior to his current position, Sikai Wang worked at Blue Shield of California in various roles. He served as a Machine Learning Intern for two months in 2019, followed by a three-year tenure as a Risk Adjustment Consultant with a focus on machine learning from 2019 to 2022. He also gained experience as a Research Assistant at the University of San Francisco School of Management for five months in 2014.
Education and Expertise
Sikai Wang holds a Bachelor's Degree in Applied Mathematics, with a focus on Financial Mathematics and Actuarial Science, from Guangdong University of Foreign Studies, completed between 2008 and 2012. He furthered his education by obtaining a Master's Degree in Financial Analysis from the University of San Francisco in 2014, and a Master of Science in Applied Statistics from California State University-Long Beach, which he completed in 2020.
Technical Skills and Proficiencies
Sikai Wang possesses advanced modeling skills in various machine learning techniques, including generalized linear models, random forests, neural networks, and support vector machines. He is proficient in multiple programming languages, including Python, R, SAS, and SQL, which he utilizes to analyze data and build predictive models.
Contributions to the Tech Community
Based in Irvine, California, Sikai Wang actively contributes to the local tech community. His background in machine learning and statistics positions him as a knowledgeable resource in the field, and he engages with peers and organizations to promote advancements in technology and data science.