Data Science Fellowship Program
The Duke AI Health Data Science Fellowship Program is a 2-year training program in data science with direct application for healthcare. Designed for early-career data scientists with strong backgrounds in quantitative discipline, the program is part of a multidisciplinary, campus-spanning initiative that applies machine learning and quantitative sciences to rich sources of healthcare and administrative data, using the insights gained to improve healthcare delivery, quality of care, and the health of individuals and communities.
Data Science Fellows are integrated into multidisciplinary teams, where they are mentored by and work under the direction of the team’s quantitative lead, who offers advanced expertise in data science and machine learning. Fellows also receive guidance and mentoring from the team’s clinical lead(s), who provide insight into the clinical and operational contexts in which projects are embedded, and in some cases receive mentoring from a staff statistician. Data science teams also include a project manager and may include an informaticist.
Duke AI Health is currently accepting applications for a Data Science Fellowship position. For details regarding job description and requirements, as well as
instructions on how to apply, please visit the following link:
Each team focuses on a well-defined research question or problem related to healthcare delivery, quality of care, or clinical operations. Working in this intense but supportive environment, trainees acquire essential skills in healthcare analytics and modern machine learning methods, while also benefiting from in-depth exposure to real-world clinical problem-solving. The Data Science Fellow works as a fully integrated member of these multidisciplinary teams, which are assembled jointly with input from AI Health, the Department of Biostatistics and Bioinformatics, Duke University Health System, and Duke School of Medicine clinical leadership.
AI Health Fellows typically work on 2 to 3 projects each year and are encouraged to collaborate across complementary projects as opportunity and topic focus align.