Corporate Director, Data Science & AI Engineering

Emory Healthcare is Georgia's most comprehensive academic health system, investing boldly in its AI-powered data future. They are seeking a Corporate Director, Data Science & AI Engineering to lead a multidisciplinary team and oversee the integration of machine learning and AI capabilities to improve patient outcomes and institutional performance.

Responsibilities:

• Provide strategic direction for the design, deployment, and lifecycle management of ML and AI models across clinical and operational use cases
• Establish and mature MLOps and LLMOps practices — including model versioning, monitoring, drift detection, and responsible AI guardrails — in collaboration with AI Engineers
• Champion the integration of Generative AI and large language model (LLM) capabilities into EHC workflows, identifying high-value use cases and ensuring safe, governed deployment
• Partner with the CDAO and CAIO Offices and Emory Digital/OIT to build a scalable, cloud-native ML infrastructure on Microsoft Fabric and Azure, enabling rapid experimentation and production-grade AI delivery
• Collaborate with the Corp Director AI Strategy, Corp Director Data Engineering, and Data & AI Governance Manager to obtain a deep understanding of stakeholder data needs across the Health System and translate those needs into a cohesive, institution-wide executable Data & AI Models
• Catalog existing data shortcomings, establish common definitions, and lead initiatives to reduce reporting redundancies and increase data access, sharing, and consumption
• Drive advanced analytics initiatives — including predictive modeling, NLP, and population health analytics — that directly inform strategic fundraising, clinical operations, and resource planning
• Proactively mine all data sources for untapped opportunities; surface patterns through data modeling that enhance EHC's Digital Data & Analytics roadmap
• Design and implement comprehensive data/AI governance policies, data quality frameworks, and data standards in collaboration with Emory partners
• Own data lifecycle management strategy: ingestion, transformation, quality, archiving, and retention across structured and unstructured datasets
• Work with legal, compliance, and IT to ensure data privacy (HIPAA, GDPR), ethical AI use, and responsible data stewardship practices are embedded in all programs
• Lead the modernization of shared data management and analytics architecture, facilitating joint collaborations that leverage Fabric-based shared infrastructure and resources
• Recruit, develop, and lead a high-performing team of AI Engineers and Data Scientists within the CDAO and CAIO Offices
• Cultivate collegial partnerships with Emory University research, IT, and academic groups to build consensus and drive shared AI/data initiatives
• Collaborate with external organizations to source and leverage third-party data assets that augment institutional analytics capabilities
• Present complex data findings, AI model outputs, and strategic recommendations to senior leadership, boards, and clinical decision-makers with clarity and conviction

Requirements:

• Bachelor's degree in data science, engineering, statistics, analytics or related areas, and ten years of related experience, OR an equivalent combination of experience, education, and training
• Knowledge about health, research, and/or academic programs
• Excellent communication skills and experience presenting findings to decision-makers
• Ability to collaborate with senior leadership, work effectively and independently on multiple priorities with strict deliverable dates
• Experience working with both business users and technical development teams
• Experience with APIs, business intelligence, and data visualization tools (experience WebGIS, RShiny, and/or Microsoft Power BI highly preferred)
• Experience with Cloud environment and services
• Familiarity with data protection and privacy, data ethics, and data governance issues
• Strong, demonstrated skills in writing and presentation of findings and analyses
• Experience working with large structured and unstructured datasets and telling a compelling story that tracks to value
• Master's degree in Data Science, Computer Science, Statistics, Engineering, or a related quantitative field; advanced degree (PhD) strongly preferred
• 10+ years of progressive experience in data science, advanced analytics, or AI/ML leadership — with at least 3 years managing technical teams in an enterprise environment
• Demonstrated expertise in machine learning lifecycle management (MLOps): model development, deployment, monitoring, and governance at scale
• Hands-on experience with cloud-native data platforms; Microsoft Fabric, Azure ML, or equivalent modern data lakehouse/warehouse architectures
• Proficiency in business intelligence and data visualization (Power BI strongly preferred); experience with APIs, Python/R, and large-scale SQL-based analytics
• Strong understanding of data governance, ethical use of Data/AI, and privacy regulations (HIPAA experience is a significant plus)
• Exceptional executive communication skills — ability to synthesize technical complexity into strategic narrative for C-suite and Board-level audiences
• Proven ability to lead cross-functional, matrixed teams and collaborate effectively with both business users and technical engineering teams
• Experience with Generative AI / Large Language Model (LLM) deployment: prompt engineering, retrieval-augmented generation (RAG), fine-tuning, and LLMOps
• Background in healthcare or life sciences data — including clinical data models (FHIR, HL7, OMOP) and EHR analytics
• Experience with Microsoft Fabric (Lakehouse, Warehouse, Data Factory, Real-Time Intelligence, Fabric AI/Copilot) in a production enterprise setting
• Familiarity with responsible AI frameworks, bias detection, explainability (XAI), and AI ethics policy development
• Experience building and scaling data science and ML platforms in regulated or academic health environments
• Certification in cloud platforms (Azure, AWS, GCP), data governance frameworks (DAMA-DMBOK), or AI/ML (Google ML, AWS ML Specialty, etc.)