Director, Data Science&AI Core

Position Overview About the Job The Director of Data Science & Digital Health provides strategic leadership for the Center for Learning Health System Sciences (CLHSS) Data Core, overseeing data architecture, clinical data management, data delivery services, AI, data science, and digital health research across the learning health system lifecycle. The Director leads a multidisciplinary team of data scientists, analysts, and research data professionals while partnering with faculty investigators, health system leaders, informatics teams, and research operations staff. The role serves as the Center's lead for data strategy, ensuring that data infrastructure, clinical data models, governance, analytics, and AI capabilities function as an integrated ecosystem that supports research, quality improvement, clinical innovation, and operational decision-making. Working closely with university and health system partners, the Director advances scalable, interoperable, and sustainable data capabilities while ensuring the efficient delivery of high-quality data and analytic services. As a member of the CLHSS leadership team, the Director helps align data, technology, and research strategy across the Center's domains, including implementation science, pragmatic trials, digital health, evidence generation, and healthcare operations, to accelerate learning and improvement across the health system. Job Duties/Responsibilities 30%: Data Strategy, Architecture & Clinical Data Stewardship Establish and lead the strategic vision for CLHSS data architecture, clinical data management, and data governance. Guide development and evolution of enterprise-aligned clinical data models, terminology standards, ontologies, and data integration approaches supporting research and operational priorities. Partner with health system, research informatics, and IT leaders to ensure alignment between CLHSS needs and broader organizational data strategies. Promote interoperability, scalability, sustainability, and reusability of data assets and infrastructure. Lead development and governance of data standards, quality frameworks, and operational processes across the Center's portfolio. Ensure effective stewardship of real-world health data, including EHR, registry, claims, imaging, patient-reported, and digital health data sources. Anticipate future infrastructure and data capability needs to support emerging research and operational priorities. 25%: Data Delivery Services Lead and continuously improve CLHSS data delivery services supporting investigators, research teams, operational partners, and external collaborators. Oversee intake, feasibility assessment, prioritization, resource allocation, and execution of data and analytic requests. Ensure delivery of high-quality, compliant, and timely data products, analytic outputs, and research support services. Develop service models, workflows, operational metrics, and performance standards that increase efficiency, transparency, and stakeholder satisfaction. Balance competing demands across a diverse portfolio of clinical research, implementation science, digital health, and operational improvement initiatives. Build scalable processes that support growth while maintaining quality and responsiveness. 25%: AI, Data Science & Digital Health Strategy Lead the Center's strategic vision for AI, advanced analytics, data science, and digital health research, including identifying emerging methods, technologies, infrastructure needs, and funding mechanisms that advance CLHSS strategic priorities. Advise investigators and leadership on opportunities to apply AI, machine learning, computational methods, and digital technologies to improve research impact and healthcare delivery. Galvanize engagement with data science staff and faculty collaborators through varied approaches to build a pool of expertise supporting active projects, incubation of ideas, and joint pursuit of funding. Guide methodological decisions regarding appropriate use of advanced analytic approaches, balancing scientific rigor, feasibility, scalability, and operational considerations. Foster development of reusable analytic frameworks, AI-enabled workflows, and scalable research solutions. Ensure that AI and data science initiatives are grounded in strong governance, ethical principles, transparency, and real-world implementation considerations. 20%: People Leadership & Organizational Development Directly supervise and develop Data Core staff, including data scientists, analysts, and related research personnel. Manage staffing plans, role development, recruitment, mentoring, and performance management processes. Foster a collaborative, innovative, and inclusive culture that promotes professional growth and accountability. Align team goals and resource deployment with evolving Center and health system priorities. Serve as the integrative leader connecting data architecture, data delivery, analytics, AI, and research operations into a unified Learning Health System capability. Partner with leaders across CLHSS domains—including implementation science, evidence synthesis, pragmatic trials, healthcare improvement, and digital health—to ensure data capabilities effectively support Center-wide goals. Qualifications Required Qualifications Bachelor's degree in a relevant field plus 8 years of related experience, or an advanced degree plus 6 years of related experience. Demonstrated experience developing and implementing strategies that align data architecture, clinical data management, data delivery services, and advanced analytics in the healthcare domain including capabilities to support research and operational objectives. At least 2 years of supervisory, managerial, or team leadership experience. Experience with clinical and real world health data (e.g., EHR, registries, claims, digital health data) and significant expertise in clinical data terminologies, ontologies and common data models. Demonstrated knowledge of data architecture, data technologies and infrastructure, clinical data models and interoperability, data governance, and data management practices. Strong project, portfolio, and stakeholder management skills, including the ability to manage competing priorities. Excellent communication skills and ability to translate technical concepts for diverse audiences. Proficiency with modern analytics and data platforms (e.g., SQL, R, various LLM solutions, Python, Databricks, Snowflake, or similar). Commitment to positive team dynamics, client satisfaction, and creating a diverse, equitable, and inclusive environment Self-motivated and self-directed; able to set priorities and drive results Preferred Qualifications Doctoral degree in a relevant field. Experience leading a research data core, analytics team, or shared research infrastructure. Experience with AI, machine learning, advanced analytics, and healthcare data interoperability standards. Experience working in an academic health center or healthcare delivery system. Success supporting or securing externally funded research. Experience in learning health systems, implementation science, pragmatic trials, digital health, or embedded research. Knowledge of regulatory and compliance requirements related to health research (e.g., HIPAA, IRB, FDA). Project management certification or equivalent leadership training. About The Department The Center for Learning Health System Sciences is a one-of-a-kind partnership between the Medical School and School of Public Health with a goal to decrease the time it takes for science to make it from the lab to the clinic in pursuit of better health outcomes for the patients we serve. CLHSS is composed of a core and five programs spanning the data-knowledge-practice LHS lifecycle including evidence synthesis, pragmatic trials, digital health, and data democratization and model development. CLHSS is committed to diversity,