Corporate Director, Data Science & AI Engineering

United States of America

Full Time

1 hour ago

No H1B

Key skills

Data ScienceArtificial IntelligenceMachine LearningMLOpsLLMOpsGenerative AILarge Language ModelsMicrosoft FabricAzure MLCloud-native ML infrastructurePower BIAPIsPythonRSQLData GovernanceHIPAAGDPRFHIRHL7OMOPEHR AnalyticsResponsible AI frameworksBias detectionExplainable AI (XAI)AI EthicsData Lifecycle ManagementData VisualizationData PrivacyData Quality FrameworksData StandardsData EngineeringData AnalyticsPredictive ModelingNatural Language Processing (NLP)Population Health AnalyticsCloud Platforms Certification AzureCloud Platforms Certification AWSCloud Platforms Certification GCPData Governance Frameworks (DAMA-DMBOK)AI/ML Certifications Google MLAI/ML Certifications AWS ML SpecialtyExecutive CommunicationCAIMLNLPLLMRAGAnalyticsBIBusiness IntelligenceAWSAzureGCPLeadershipResource PlanningCommunicationCollaboration

About this role

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.)