Jingyuan Liu
About Jingyuan Liu
Jingyuan Liu is a Data Scientist at Komodo Health with a background in data engineering and electrical engineering.
Current Role at Komodo Health
Jingyuan Liu currently works at Komodo Health as a Data Scientist. He joined the company in June 2021. In this role, he applies his extensive background in data science and engineering to contribute to the company's healthcare data analytic initiatives.
Previous Experience at Aetna
Before joining Komodo Health, Jingyuan Liu worked at Aetna, a CVS Health Company. He served as a Senior Data Engineer for 9 months from 2020 to 2021 in New York, United States. Prior to that, he was a Data Engineer at Aetna for 1 year starting from 2019 in the Greater New York City Area, showcasing a steady career progression within the company.
Academic Background in Electrical Engineering
Jingyuan Liu holds a Master's degree in Electrical Engineering from Columbia University in the City of New York, which he completed between 2017 and 2018. Additionally, he earned a Bachelor's degree in Electrical Engineering from Shanghai Jiao Tong University, where he studied from 2013 to 2017. He also attended a summer program at the Georgia Institute of Technology in 2015, where he earned a 4.0 GPA.
Internship and Early Career Experience
Jingyuan Liu has accumulated varied internship experiences early in his career. In 2018, he interned as a Data Engineer at IoT Nation for 2 months in the Greater New York City Area. Prior to that, he was a Global Delivery & Consulting Intern at GEP Worldwide for 2 months in 2017 in Shanghai City, China. He also served as an NS Intern at Schneider Electric Industrial Services for 2 months in 2016 and as a CMO Assistant at Qing Flow for 4 months from 2016 to 2017, both in Shanghai.
Teaching Assistant Experience at Columbia University
Jingyuan Liu served as a Statistical Learning Teaching Assistant at Columbia University in the City of New York for 3 months in 2018. This role likely involved assisting in teaching courses, grading assignments, and aiding students in understanding complex statistical learning concepts.