Jing Gong
About Jing Gong
Jing Gong is a Director and Statistician at Bristol Myers Squibb in New Jersey, with a strong background in Chemistry and Biostatistics.
Current Role at Bristol Myers Squibb
Jing Gong currently holds the position of Director, Statistician at Bristol Myers Squibb in New Jersey, United States. He commenced his role in January 2020. In his capacity as a statistician, he contributes to various projects and initiatives, leveraging his extensive background in biostatistics and statistics within a leading pharmaceutical company.
Previous Experience at Celgene
Before his current role, Jing Gong served as Associate Director, Statistician at Celgene from 2015 to 2019. Throughout his four-year tenure at Celgene, which also took place in New Jersey, he engaged in a multitude of tasks that further honed his analytical skills and bolstered his expertise in the pharmaceutical industry. His role there provided a solid foundation and valuable experiences that he has carried forward into his current position.
Educational Background
Jing Gong's educational journey is marked by comprehensive training in various scientific disciplines. He earned his Ph.D. in Biostatistics from the School of Public Health at Rutgers University. Additionally, he holds a Master of Science in Statistics from Rutgers University. Prior to these accomplishments, he obtained a Master’s and a Bachelor’s degree in Chemistry from Fudan University. His academic journey began at Hefei No. 6 Middle School. This extensive and diverse educational background underpins his strong analytical capabilities and his proficiency in biostatistics.
Expertise in Chemistry and Biostatistics
Jing Gong's expertise spans multiple scientific fields, including chemistry and biostatistics. His dual focus on these areas is supported by advanced degrees in both disciplines. This combination of skills enhances his ability to tackle complex problems and design robust statistical models, which are crucial in both academic research and the pharmaceutical industry. His work often bridges the gap between statistical theory and practical application in medical sciences.