袁夕宁
About 袁夕宁
袁夕宁 is a research assistant at Yale University, specializing in biostatistics. With a strong background in mathematics and experience in the healthcare industry, he has developed skills in model building using Python.
Work at Yale University
Currently, 袁夕宁 serves as a research assistant at Yale University in New Haven, Connecticut. This position has been held since 2021, contributing to various research projects and initiatives within the institution. The role involves applying analytical skills and knowledge gained from previous experiences to support research efforts.
Education and Expertise
袁夕宁 holds a Bachelor's degree in Mathematics from Rutgers University - Newark, completed from 2017 to 2020. Following this, 袁夕宁 pursued further education at Yale University, specializing in biostatistics from 2020 to 2022. This educational background provides a strong foundation in quantitative analysis and statistical methodologies.
Background in Healthcare Industry
袁夕宁 gained valuable experience in the healthcare sector during an internship at Deloitte in Beijing. This role provided insights into healthcare analytics and industry practices. Additionally, 袁夕宁 worked as a medical industry research analyst at 金建(深圳)投资管理中心(有限合伙) for three months, further enhancing expertise in healthcare research.
Professional Experience in Data Analysis
袁夕宁 has a diverse professional background, including roles as a data analyst at GT Financial Advisors, LLC in New York, where skills in model building using Python were developed. Previous internships include a position at Deloitte in Beijing and a short-term role at 中金公司. These experiences contributed to a solid understanding of data analysis and financial modeling.
Skills Development and Learning
袁夕宁 demonstrates a strong ability to quickly master new skills and enjoys continuous learning. This adaptability is evident in the transition from academic studies to practical applications in research and data analysis. The commitment to skill development is a key aspect of 袁夕宁's professional approach.