Manager - AI Strategy & Operations - MUSCP

Primary Areas of Responsibility

• AI Opportunity Discovery & Operational Strategy – 35%: Identify operational workflows poised for AI-driven efficiency gains or cost savings. Lead discovery workshops and needs assessments with departments across MUSC. Build opportunity maps, prioritize initiatives, and align with enterprise strategic goals. Develop cadenced maintenance, and AI Fine-Tuning, Bias testing, Audit, etc. Identify existing AI Solutions primed for enhancement, build a pipeline visible to senior officers; base targeted workflows based on impact index to enterprise.
• Team Leadership & Workforce Development – 25%: Lead a team of Jr. Data Scientists, ML Engineers, and a Jr. Architect. Provide mentorship, performance management, and resource planning. Develop skill-building programs and promote a collaborative AI culture.
• Solution Development & Deployment Support – 20%: Oversee scaling of AI prototypes and solutions into enterprise environments. Guide production readiness planning and ensure operational requirements are met. Ensure compliance with AI governance, quality standards, and risk controls. Process document, and orient pre-assessment deployment evaluations for senior leadership review. ROI Measurement & Executive Reporting – 10%: Document and quantify financial and operational ROI from AI implementations measure as indirect or direct returns in revenue. Track performance metrics and present outcomes to senior leadership at quarterly and annual cadence.
• Partner Engagement & Thought Leadership – 10%: Advise departmental leaders on AI feasibility, adoption readiness, and workflow fit. Provide professional assessments to partners of build vs. buy. Serve as an institutional subject matter expert for applied operational AI. Showcase examples of intentional design, and successful implementations to encourage further AI application. Eliminate barriers and advocate for streamlined AI integration.

Required Qualifications

• Education: Master’s degree required; PhD preferred.
• Experience: 3–5 years of relevant experience with leadership exposure in data science, AI, analytics, operational improvement, or technical program management.
• Technical Capability: Experience collaborating with data scientists, ML engineers, or technical teams; familiarity with applied AI/ML preferred.
• Leadership: Demonstrated ability to supervise teams and manage cross-functional projects.
• Operational Expertise: Proven experience partnering with operational or clinical leaders to identify efficiency improvements or workflow enhancements.