Johan Edvinsson
About Johan Edvinsson
Johan Edvinsson is a Machine Learning Engineer at Hyperscience with extensive experience in NLP and data science roles across various organizations in the Greater New York City Area.
Title
Johan Edvinsson is a Machine Learning Engineer currently employed at Hyperscience, where he has been working since 2021. His expertise focuses on machine learning and natural language processing technologies.
Professional Experience at Hyperscience
Johan Edvinsson joined Hyperscience as a Machine Learning Engineer in 2021. His role involves leveraging advanced machine learning techniques to improve data automation processes. His work in the New York City Metropolitan Area is geared towards enhancing the efficiency and accuracy of data processing systems utilized by the company.
Prior Roles at Oscar Health
From 2019 to 2021, Johan Edvinsson served as a Senior NLP and Machine Learning Engineer at Oscar Health. During his tenure, he focused on deploying natural language processing and machine learning solutions to assist health plan members in finding necessary answers and healthcare services efficiently.
Experience at Condé Nast
Between 2016 and 2019, Johan Edvinsson worked at Condé Nast as an NLP and Machine Learning Engineer within the Content Intelligence team. He developed several internal APIs for natural language processing and machine learning applications, including topic modeling, keyphrase extraction, text-complexity analysis, content recommendation, and image processing.
Academic Background and Early Career
Johan Edvinsson holds a Ph.D. in Physiology, Biophysics, and Systems Biology from Joan & Sanford I. Weill Medical College of Cornell University, awarded in 2010. His doctoral studies spanned from 2003 to 2010, during which he also served in various roles at Weill Cornell Medical College, such as Graduate Teaching Assistant and Journal Club Organizer. Prior to this, he completed his Bachelor of Science in Athletic Training/Exercise Physiology at Towson University from 1998 to 2002.