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Provides executive-level administrative and coordination support to Mayo Clinic Education leadership. Responsibilities include calendar management, special projects, information prioritization, confidential information handling, cross-functional coordination, and ensuring executive responsiveness. The role requires sound judgment, initiative, strong organization, communication skills, adaptability, and proficiency with office productivity and collaboration tools.

Leads Mayo Clinic’s enterprise AI and robotics strategy, governance, portfolio prioritization, operational enablement, and workforce adoption. The role develops multi-year roadmaps, establishes responsible AI and interoperability standards, partners with clinical, technical, legal, facilities, and security stakeholders, and oversees procurement and vendor governance. As a people leader, the Director manages operations managers, execution processes, reporting, budgets, and cross-functional initiatives while advising senior executives on AI strategy and value realization.

Serve as the senior technical authority for Mayo Clinic’s enterprise robotics architecture. Define standards, reference architectures, vendor evaluation frameworks, interoperability, data governance, observability, orchestration, security, safety, and reliability for heterogeneous robot fleets. Establish multi-year technical direction for AI-driven autonomous, generalist, and humanoid systems in a regulated healthcare environment.

Leads technical business analysis for digital products and AI program initiatives. Defines and validates business, operational, system, technical, user experience, and integration requirements; performs cost, benefit, risk, and root-cause analysis; translates requirements into engineering specifications and acceptance criteria; supports product deployment and change management; and mentors business analysts while advancing analysis practices, standards, and governance.

Owns the end-to-end vision, strategy, roadmap, requirements, execution, and performance of a complex digital product within an AI program. Leads cross-functional teams across product, design, engineering, analytics, marketing, clinical, and operational partners. Defines OKRs, manages backlogs, budgets, schedules, risks, vendors, stakeholder alignment, user research, product discovery, and adoption outcomes while ensuring delivery supports business goals and user needs.

Provides non-clinical administrative support for a laboratory department, including calendar and meeting coordination, travel and expense processing, document preparation, correspondence, telephone support, database and spreadsheet maintenance, website and social media updates, and departmental project assistance. Requires accuracy, confidentiality, professionalism, strong organization, adaptability, and a two-year commitment.

Develops and advances AI and machine learning solutions for digital pathology and multimodal clinical data. Builds, evaluates, validates, and deploys predictive models and clinically deployable products in collaboration with clinical, technical, regulatory, and software teams. Establishes practices for model quality assurance, reproducibility, monitoring, and regulatory documentation. Analyzes complex healthcare datasets, translates clinical needs into AI solutions, provides enterprise consultation, communicates insights, and mentors team members.

Supports Mayo Clinic business development and investment activities by evaluating opportunities, conducting financial modeling, valuation, due diligence, market research, and scenario analysis. Develops business plans, reports, and strategic materials; coordinates with internal teams, investment committees, executives, entrepreneurs, and external partners. Responsibilities may also include technology commercialization, licensing, intellectual property analysis, partnership development, portfolio management, governance, and post-deal reporting.

Develops advanced AI, machine learning, statistical, predictive, prescriptive, generative, and multimodal models using heterogeneous healthcare data. Applies LLMs and foundation models to clinical applications, extracts insights from structured and unstructured data, collaborates with clinicians and stakeholders, translates technical findings for nontechnical audiences, and leads data interpretation, reporting, consulting, and project work in healthcare settings.

Leads advanced data science and AI initiatives using multimodal healthcare data, including imaging, genomics, clinical text, EHRs, and signals. Develops predictive, prescriptive, generative, and multimodal models; adapts and evaluates LLMs and foundation models; designs algorithms and experiments; and translates findings into healthcare applications. Provides enterprise consulting, presents results, leads technical teams and scientific projects, and collaborates with clinicians, informaticians, knowledge architects, and external partners.