Deputy CIO – Chief Enterprise Insights

Job Description Summary

The Chief Enterprise Insights Officer (CEI) is a transformational executive responsible for partnering with enterprise leadership to advance MUSC’s transition into a fully data-enabled, insights-driven organization. The CEI will lead the development of a foundational data ecosystem with its embedded AI architecture that connects MUSC’s missions across clinical care, research, education, and operations, and will lead the development of MUSC’s Intelligent Data Platform, integrating an AI overlay.

Entity

Medical University Hospital Authority (MUHA)

Worker Type

Employee

Worker Sub-Type​

Regular

Cost Center

CC002269 SYS - IS Senior Leaders

Pay Rate Type

Salary

Pay Grade

Health-00

Scheduled Weekly Hours

40

Work Shift

Job Description

The Chief Enterprise Insights Officer (CEI) is a transformational executive responsible for partnering with enterprise leadership to advance MUSC’s transition into a fully data-enabled, insights-driven organization. The CEI will lead the development of a foundational data ecosystem with its embedded AI architecture that connects MUSC’s missions across clinical care, research, education, and operations, and will lead the development of MUSC’s Intelligent Data Platform, integrating an AI overlay.

Evolving from the Chief Data Analytics Officer role, the CEI shifts MUSC from analytics delivery to enterprise-wide insights enablement. The CEI will steward the Enterprise Data Foundry, build next-generation capabilities in data engineering, AI/ML operations, and cloud platforms, and integrate solutions across federated data environments, research computing, educational technologies, and population health analytics.

This leader will co-develop a business-led, system-level data governance framework and refine the current Data and AI Governance model to align with enterprise priorities. The CEI will establish policies for the security and compliance of MUSC’s data ecosystem while fostering a culture of data literacy, workforce development, and evidence-based decision-making.

Reporting & Scope:

· Reports to: Enterprise Chief Information Officer (CIO)

· Direct leadership: Leaders of Data Engineering, Analytics/BI, AI/ML Ops, Data Governance/MDM, Research Computing/HPC, and EdTech/Academic Analytics

· Decision rights (shared with Legal/Compliance/CISO as appropriate): Enterprise data & AI standards; model approval & monitoring; data-sharing agreements; platform selection; insights program budget & portfolio

· Work model: Hybrid, Charleston, SC (flexible within MUSC policy); Travel: up to ~20% across SC partners/affiliates

Key Responsibilities:

Strategic Leadership & Vision (20%)

· Define and champion MUSC’s enterprise insights vision, positioning data, AI, and automation as strategic enablers of the tripartite mission and statewide health leadership.

· Establish and evolve the Enterprise Data Foundry as MUSC’s trusted, scalable, and interoperable foundation for innovation.

· Develop a multi-year insights roadmap balancing foundational capabilities with transformational use cases (precision medicine, population health, digital education).

· Anticipate emerging technologies and industry trends and proactively incorporate them to maintain MUSC’s leadership position.

Mission Enablement Responsibilities (20%)

· Clinical Mission – Embed predictive and AI-driven insights into Epic and digital care models to advance population health, precision medicine, quality, and value-based care.

· Research Mission – Oversee core data architecture, data ecosystem health, and foster statewide/national data consortia.

· Academic Mission – Advance learning analytics and integrate insights into academic governance and practice

· Operational Mission – Optimize workforce, finance, and operations; modernize platforms and M&A integration; expand automation and analytics to improve efficiency, patient experience, and ROI.

Technology & Platform Enablement (20%)

· Build and oversee Data Engineering, DataOps, and Cloud Operations teams, ensuring agility and reliability.

· Lead adoption of best-in-class platforms (Databricks-class analytics, Snowflake-class integration, Watsonx-class AI governance, Google HDE-class scale).

· Advance MDM transformation and enterprise data stewardship.

· Champion interoperability (FHIR APIs, HL7) to link Epic, academic, and research systems; enable federated learning and distributed models.

· Co-evaluate and integrate emerging technologies (generative AI, edge computing, synthetic data) to ensure future-readiness.

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