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. 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 deep learning, large language models and -multimodal modeling for large scale clinical and biomedical data. large language models and multimodal architectures. -Translate research findings into deployable systems, decision-support tools, or analytical frameworks. -Conduct external validation and real-world evaluation of machine learning models using diverse clinical datasets. -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. -Develop reproducible data pipelines and collaborative model development environments. -Support the integration of AI tools into ongoing clinical and translational research workflows.