Data Scientist- AI.HealthAll

Position Summary The Data Scientist will lead and contribute to the design, development, and implementation of artificial intelligence (AI) and machine learning (ML) solutions that advance healthcare delivery, clinical decision-making, and population health research with attention to model generalizability, fairness, and performance across diverse patient populations. This role integrates data science, software engineering, and translational research, with a focus on reproducibility, fairness, and real-world clinical impact. The data scientist will serve as a technical and scientific lead, collaborating with faculty, clinicians, and data engineers to deliver impactful applied research projects. The position also includes mentorship of junior researchers and fostering a high standard of technical and scientific excellence across the program. Duties Responsibilities Research and Technical Leadership -Lead applied AI and ML research projects in healthcare and biomedical domains. -Develop, fine-tune, and evaluate advanced machine learning models, including large language models and multimodal architectures. -Translate research findings into deployable systems, decision-support tools, or analytical frameworks. -Ensure all research outputs adhere to best practices in reproducibility, documentation, and data integrity. Collaboration and Grant Development -Collaborate with clinical investigators and interdisciplinary teams to identify opportunities for AI-driven solutions. -Contribute to manuscripts, technical reports, and grant proposals, and institutional pilot mechanisms. -Present project outcomes in academic and professional settings. Mentorship and Supervision -Supervise and mentor junior data scientists, research associates, and students. -Provide guidance on experimental design, software development, and machine learning methodology. -Promote a collaborative and transparent research environment. Infrastructure and Innovation -Develop and maintain data processing pipelines, model repositories, and evaluation tools. -Implement scalable and secure computing environments that comply with institutional data-governance and privacy policies. -Support the integration of AI tools into ongoing clinical and translational research workflows.