Corporate Director, Data Science & AI Engineering

Emory Healthcare•Tucker, GA

8h•Hybrid

About The Position

Emory Healthcare (EHC) — Georgia's most comprehensive academic health system — is investing boldly in its AI-powered data future. As we modernize our enterprise data platform on Microsoft Fabric and scale AI capabilities across clinical, research, and operational domains, we are searching for an exceptional leader to serve as Corporate Director, Data Science & AI Engineering.Dual reporting to the Chief Data & Analytics Officer (CDAO) and the Chief AI Officer (CAIO), this role is the connective tissue between enterprise strategy and AI/ML execution. You will own the data science management vision, lead a multidisciplinary team that includes AI Engineers and Data Scientists, and serve as the primary architect of how Emory Healthcare harnesses advanced machine learning, generative AI, and large-scale analytics to improve patient outcomes and institutional performance.This is not a theoretical strategy role. We expect our Director to be equally fluent in designing data Science and AI frameworks, deploying ML models into production, overseeing LLMOps pipelines, and translating complex analytical findings into boardroom-ready strategy — all within a dynamic, mission-driven health system environment.

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.

Nice To Haves

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.).

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.