Research Assistant in Interdisciplinary Metrics at Section for Health Data Science and AI

This Research Assistant role in Interdisciplinary Metrics within the Section for Health Data Science and AI requires foundational knowledge and skills at the intersection of health data science, artificial intelligence, and metrics development. The ideal candidate should possess strong analytical abilities to work with complex health datasets and apply AI methodologies to derive meaningful insights. Familiarity with interdisciplinary research approaches is important, enabling collaboration across domains such as biostatistics, computer science, epidemiology, and health informatics. Proficiency in programming languages commonly used in data science, such as Python or R, and experience with AI frameworks or libraries would be beneficial. The role likely involves data preprocessing, statistical analysis, metric design, and validation within health-related contexts. Attention to detail, critical thinking, and effective communication skills are essential for contributing to research projects and disseminating findings. While specific experience and education levels are not detailed, candidates with a background in health data science, AI, or related quantitative fields, and some research experience, would be well-suited. Overall, this position demands a combination of technical expertise in data science and AI, interdisciplinary collaboration capabilities, and a commitment to advancing health metrics research.