Director, Data and AI

St. Jude Children's Research Hospital•Remote - TN, TN

4d•$138,320 - $262,080•Remote

About The Position

The Director, Data and AI will lead the strategy, continued implementation, and adoption of St. Jude’s enterprise Databricks platform on Azure. The role makes trusted, governed, AI-ready data a routine part of work across research, administration, operations, and clinical integration, translating platform capabilities into measurable institutional and mission impact. The position leads data engineers, AI engineers, product leads, platform architects, and solutions engineers, and advises the Vice President, Data & Analytics on platform direction and investment. The Director will deliver reusable data products and production AI solutions, establish platform and operating standards, and work with data governance, security, privacy, compliance, legal, clinical, research, and institutional AI governance partners. The role also drives user adoption through discovery, onboarding, training, champions, self-service tools, and retirement of redundant reports and legacy tools. It oversees product delivery, vendor and budget management, value measurement, and team development. The position may be remote within approved U.S. locations. Remote employees must travel to the Memphis campus for interviews and/or orientation if selected, and work on-site in Memphis for one week per quarter or as business needs require. Applicants residing in California are not eligible for remote work, though those willing to relocate to an approved location may be considered.

Requirements

Bachelor’s degree in business administration, computer science, data science, information science, or a related field.
At least 10 years of significant experience in an IT functional area, managing mid-to-large teams while achieving objectives and building critical skills.
Experience leading large-scale projects or processes through implementation.
Experience with operational management, budget planning, vendors, and resource management.
Prior experience building external connections with peers, emerging technology communities, and vendor networks.
Proven performance in an earlier or comparable role.
Enterprise Databricks expertise, preferably on Azure, including Delta Lake, Apache Spark, Lakeflow or Delta Live Tables, Databricks SQL, workflows and orchestration, Unity Catalog, MLflow, Mosaic AI or model serving, Vector Search, Genie, and agent frameworks.
Azure architecture knowledge covering security, Entra ID, networking, storage, private connectivity, cost management, capacity planning, and FinOps.
Proficiency in Python and SQL, with experience in CI/CD, Git, infrastructure as code such as Terraform, DevOps, DataOps, automated testing, observability, and production support.
Data architecture and modeling expertise, including lakehouse and medallion design, dimensional modeling, batch and streaming ingestion, and integration with enterprise, research, and clinical source systems.
Knowledge of AI-ready data product delivery, including semantic layers, metadata, lineage, data contracts, quality, structured and unstructured data preparation, embeddings, retrieval, and knowledge grounding.
Knowledge of MLOps and LLMOps, including evaluation, monitoring, versioning, guardrails, human oversight, incident response, and lifecycle management.
Experience operating data and analytics as a product practice and implementing data governance in a modern platform.
Knowledge of HIPAA and privacy practices, including de-identification and access controls for sensitive clinical and research data.
Strategic thinking and roadmap-planning ability, including evaluating emerging data, platform, and AI capabilities and advising executive leaders on investment and direction.
Strong executive presence and presentation skills, with ability to explain complex technical concepts clearly to executives, boards, clinicians, scientists, and external audiences.
Excellent written and verbal communication, stakeholder engagement, emotional intelligence, sound judgment, and ability to influence across technical, clinical, scientific, administrative, and operational groups.
Experience speaking at conferences, publishing, or representing an organization in external data and AI collaborations.

Nice To Haves

Master’s degree.
Ten or more years of experience in data engineering, data platforms, AI and ML engineering, analytics, architecture, or related technology disciplines, including at least five years leading teams and managing budgets.
Demonstrated success leading enterprise data platform implementation and adoption with measurable growth in users, use cases, certified data products, and mission or business outcomes.
Experience leading blended teams across engineering, architecture, product management, platform operations, and solutions delivery.
Experience establishing enablement programs, federated or hub-and-spoke operating models, and data champion networks.
Experience moving machine learning or generative AI solutions from pilot to reliable production operation.
Experience managing vendors, consulting partners, contracts, budgets, and cloud investments.
Experience in healthcare, life sciences, biomedical research, an academic medical center, pediatric medicine, or oncology.
Experience with enterprise systems such as Epic, Workday, Adaptive Planning, Visier, HR and workforce management, CRM, ServiceNow, and financial planning platforms.
Databricks or Microsoft Azure certifications.
Experience with EHR data and healthcare standards such as Epic, Cerner, HL7, FHIR, OMOP, SNOMED CT, LOINC, RxNorm, and DICOM.
Experience with genomic, multi-omics, imaging, high-performance computing, or GPU-enabled research environments.
Experience supporting open science and secure data sharing with external collaborators, including cloud-based research environments and controlled-access data.
Experience with ontologies, knowledge graphs, or biomedical vocabularies that improve data interoperability and AI accuracy.
Certification or formal training in product management, agile delivery, or organizational change management.

Responsibilities

Own the strategy, roadmap, and continued implementation of the enterprise Databricks platform on Azure, including architecture, pipelines, workspaces, security, identity, networking, reliability, performance, capacity, and cost management.
Set enterprise platform standards, reusable reference patterns, technical decisions, and operating practices with platform architects.
Deliver governed, reusable data products across research, administration, and operations, integrating clinical data in partnership with the Director of Clinical Data and Analytics.
Evaluate and introduce Databricks and AI capabilities when they address institutional needs, including AI/BI Genie, Agent Bricks, Lakeflow, Lakebase, Mosaic AI, MLflow, model serving, and Vector Search.
Manage platform vendors, consulting partners, cloud providers, contracts, budgets, service levels, and FinOps practices.
Ensure priority data products are trusted, documented, governed, discoverable, reusable, and ready for people and AI systems; establish and track certification and AI-readiness measures.
Develop semantic layers, metadata, lineage, ontologies where appropriate, quality standards, and preparation of structured and unstructured data for AI use.
Deliver research data capabilities for genomic, multi-omics, imaging, clinical, and other scientific data in partnership with the Office of Data Science and in support of open science.
Deliver analytics, data products, and AI for administrative functions including finance, human resources, legal, and compliance.
Deliver operational solutions for capacity, scheduling, supply chain, facilities, and patient and family experience, including predictive and optimization models.
Partner with the Director of Clinical Data and Analytics to connect clinical, research, and operational data and establish shared strategies, priorities, governance, ownership, and service expectations while preserving clinical domain accountability.
Define and lead enterprise AI strategy across machine learning, generative AI, and agentic applications in alignment with institutional governance and the Office of Data Science.
Lead AI engineers in developing and deploying production machine learning models, LLM applications, retrieval-augmented generation, and agents on Databricks.
Establish MLOps and LLMOps practices for versioning, evaluation, monitoring, observability, drift detection, incident response, rollback, and safe promotion to production.
Operationalize responsible AI with governance partners, including privacy, security, model risk review, bias and fairness testing, human oversight, and auditability.
Translate governance policies and stewardship practices into platform controls such as lineage, classification, access control, quality rules, metadata, monitoring, and audit logging.
Work with Information Security, Privacy, Compliance, Legal, and research oversight partners to protect PHI and research data under HIPAA, IRB and consent requirements, NIH data-sharing policies, and applicable controlled-access requirements.
Empower product leads to manage roadmaps, intake, prioritization, user discovery, delivery, adoption, and outcomes, including AI readiness and adoption in product definitions of done.
Prioritize investments by mission impact, user value, feasibility, risk, and total cost; maintain transparent demand management, delivery, and value-realization practices.
Drive platform adoption by setting targets, engaging users, reducing access and onboarding friction, providing ready-to-use data products and tools, and creating training, office hours, and communities of practice.
Build a federated network of embedded partners and data champions; consolidate redundant reports, data marts, and shadow tools; measure and communicate platform value and outcomes.
Mentor and coach team members, develop career paths and successors, support professional growth, and cultivate a psychologically safe, inclusive, accountable, and learning-oriented culture.
Represent data and AI to executives, governance bodies, collaborators, peer institutions, academic partners, and vendors; communicate technical topics in mission and business terms.