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. Minimum Qualifications PhD. in Computer Science, Electrical Engineering, Biomedical Informatics, or a related quantitative field. Strong record of research in applied machine learning, natural language processing, or related AI domains. Proficiency with modern ML frameworks such as Py. Torch, or Tensor. Flow. Demonstrated programming expertise in Python and familiarity with distributed or cloud-based computing. Experience working with large or complex datasets, preferably in healthcare or biomedical contexts. Evidence of peer-reviewed publications or equivalent research outputs. Excellent communication and collaboration skills within multidisciplinary teams. Preferred Qualifications Experience leading or coordinating research and technical teams. Prior work with clinical or biomedical data, including electronic health records or medical text. Familiarity with data privacy, ethical AI practices, and reproducibility standards. Demonstrated success in contributing to or securing research funding.