Manager - AI Incubation Team - MUSCP
Indexed description
Entity
University Medical Associates (UMA) Only Employees and Financials
Worker Type
Employee
Worker Sub-Type
Classified
Cost Center
CC005532 EVPAA - Center for Artificial Intelligence
Pay Rate Type
Salary
Pay Grade
Health-35
Scheduled Weekly Hours
40
Work Shift
Job Description
Primary Areas of Responsibility (with % Allocation)
- Concept Intake, Evaluation & Prioritization – 30%
- Evaluate AI ideas for enterprise value, feasibility, and alignment to MUSC priority areas.
- Build a multi‑criteria prioritization process for incubation candidates.
- Serve as the key decision‑maker for scoping and defining novel concepts.
- Incubation Team Leadership – 25%
- Manage a team of Jr. Data Scientists and ML Engineers.
- Ensure balanced allocation across multiple incubation projects.
- Foster an innovative, experimentation‑friendly environment.
- Prototype Development & Technical Oversight – 25%
- Oversee feasibility testing, rapid prototyping, data assessment, and validation.
- Ensure prototypes are developed using responsible, ethical, and compliant AI practices.
- Document technical frameworks and readiness criteria for downstream teams.
- Transition to Deployment & Stakeholder Integration – 15%
- Coordinate with Strategy & Ops and Clinical Data teams to ensure smooth transition of validated prototypes.
- Prepare design packets, implementation recommendations, and risk notes for deployment teams.
- Innovation Leadership & Enterprise Impact – 5%
- Maintain a pipeline of emerging AI opportunities across clinical, research, operational, and academic domains.
- Serve as the Center’s innovation engine to push the boundaries of applied healthcare AI.
- Seek to patent, and implement novel concepts into active execution and royalty streams for MUSC.
- Deliver 2–3 validated AI innovations annually (deployed, validated, or patentable).
- Increase MUSC’s internal capacity for early‑stage AI exploration.
- Reduce barriers preventing departments from pursuing high‑impact AI ideas.
- Education: Master’s degree required; PhD preferred.
- Experience: 3–5 years of relevant experience with leadership exposure in AI, ML, data science, research engineering, or innovation environments.
- Technical Capability: Hands‑on experience supporting AI projects, prototyping, data analysis, or early‑stage concept development.
- Leadership: Demonstrated ability to lead technical contributors (data scientists, ML engineers).
- Innovation Skillset: Experience in rapid experimentation, feasibility analysis, or proof‑of‑concept development strongly preferred.
Medical University of South Carolina participates in the federal E-Verify program to confirm the identity and employment authorization of all newly hired employees. For further information about the E-Verify program, please click here: http://www.uscis.gov/e-verify/employees
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