Director, Data and AI

Help Us Turn Extraordinary Data into Cures

St. Jude is where those with a passion for making a difference come to break new ground. Located in Memphis, Tennessee, the mission of St. Jude Children’s Research Hospital is to advance cures, and means of prevention, for pediatric catastrophic diseases through research and treatment.

Every day, St. Jude generates some of the most valuable data in pediatric medicine, including genomic and multi omics data, medical images, clinical records, and the operational detail of running a hospital and research institute. We are seeking a Director of Data and AI to take that data further and help make trusted, AI ready data part of how St. Jude works.

This leader will guide the next phase of our enterprise Databricks platform on Azure and drive adoption across research, administration, operations, and clinical integration. The Director will lead a talented team of data engineers, AI engineers, product leads, platform architects, and solutions engineers, translating platform capability into measurable mission impact.

This position may be eligible for remote work. However, St. Jude requires all remote employees to:

Travel to our Memphis campus for the interview process and/or orientation, if selected

Travel to Memphis to work on-site for one week per quarter, or as requested based on business needs, if hired.

By applying, you acknowledge and agree to these travel requirements as a condition of employment.

This position is remote within approved U.S. locations. Due to state-specific employment regulations, we are unable to consider applicants currently residing in California for remote work; however, candidates based in California who are willing to relocate to an approved location may be considered.

Your Top Priority: Make Our Data Platform the Way St. Jude Works

We have built a strong foundation on Databricks. Your central goal is to make it the first place teams turn for trusted data, analytics, and AI. A platform creates value when people use it and when it keeps pace with what they will need next, so you will lead both the overall data platform strategy and the adoption strategy that makes it matter across the institution.

Set the platform strategy and stay a few steps ahead. Keep a clear roadmap of what to enable next, anticipate where research, clinical, and operational needs and AI capabilities are heading, and advise the Vice President, Data & Analytics on where we need to go.

Set the adoption strategy. Define success and establish targets for active users, teams onboarded, certified data products in use, self service usage, AI solution adoption, and legacy reports and tools retired.

Know your users. Engage scientists, clinicians, finance and HR teams, and operational leaders to understand what they need, what slows them down, and what would make them change how they work.

Make it easy. Remove friction in access, onboarding, and tooling. Provide ready to use data products, templates, and natural language tools that help users get answers without waiting for a custom build.

Build a network of champions. Create a federated model of embedded partners and data champions who teach, support, and advocate within their departments.

Teach and enable. Launch training, office hours, communities of practice, and clear guidance on data tools and responsible AI use for every level of the organization.

Retire the old to make room for the new. Consolidate redundant reports, data marts, and shadow tools so teams have fewer, better, and more trusted sources.

Show the value. Measure and communicate time saved, decisions improved, research accelerated, operational performance improved, and legacy technology retired.

What You Will Lead

Build the Platform That Powers the Institution:

Own the strategy, roadmap, and continued implementation of the enterprise Databricks platform on Azure, including lakehouse architecture, pipelines, workspaces, security, identity, networking, reliability, performance, capacity, and cost management.

Set direction with platform architects on enterprise standards, reusable reference patterns, technical decisions, and operating practices that are secure, scalable, supportable, and designed to last.

Deliver governed, reusable data products across research, administration, and operations. Integrate clinical data in close partnership with the Director of Clinical Data and Analytics.

Assess and introduce capabilities such as AI/BI Genie, Agent Bricks, Lakeflow, Lakebase, Mosaic AI, MLflow, model serving, and Vector Search where they solve real institutional problems.

Manage platform vendors, consulting partners, cloud providers, contracts, budgets, service levels, and FinOps practices.

Deliver AI Ready Data Products:

Make each priority data product trusted, documented, governed, discoverable, reusable, and ready for use by people and AI systems.

Build semantic layers, metadata, lineage, ontologies where appropriate, and quality standards that allow tools such as Genie and AI agents to provide accurate and explainable answers.

Prepare structured and unstructured data for AI use, including document extraction, embeddings, vector search, retrieval pipelines, and knowledge grounding.

Establish measurable criteria for certified and AI ready data products and track the number, adoption, quality, and business value of priority assets.

Deliver Data and AI That People Actually Us e:

Research: Give scientists scalable, reproducible access to genomic, multi omics, imaging, clinical, and other scientific data in partnership with the Office of Data Science and in support of St. Jude’s open science mission.

Administration: Deliver data products, analytics, and AI for finance, human resources, legal and compliance.

Operations: Deliver solutions for capacity, scheduling, supply chain, facilities, and patient and family experience, including predictive and optimization models.

Clinical integration: Work closely with the Director of Clinical Data and Analytics to connect clinical, research, and operational data into a cohesive enterprise ecosystem. Together, establish shared strategies, priorities, governance, ownership, and service expectations that enable questions spanning traditional organizational boundaries while preserving clear clinical domain accountability.

Make St. Jude an AI Enabled Organization:

Define and lead the 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 building and deploying production solutions on Databricks, including machine learning models, LLM applications, retrieval augmented generation, and agents.

Establish MLOps and LLMOps practices for model and prompt versioning, registry, evaluation, monitoring, observability, drift detection, incident response, rollback, and safe promotion from pilot to production.

Partner with the Senior Director of Data Governance and institutional AI governance bodies to operationalize responsible AI, including privacy, security, model risk review, bias and fairness testing, human oversight, and auditability.

Work Hand in Hand with Data Governance:

Turn policies, standards, and the stewardship model established with the Senior Director of Data Governance into working platform controls, including lineage, classification, access control, quality rules, metadata, monitoring, and audit logging.

Define certified and AI ready data products, ownership, service expectations, quality thresholds, and compliance measures together.

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.

Run Delivery Like a Product Organization:

Empower product leads to own roadmaps, intake, prioritization, user discovery, delivery, adoption, and outcomes for each domain, with AI readiness and adoption included in every product’s definition of done.

Prioritize investments by mission impact, user value, feasibility, risk, and total cost, and sequence them against the platform roadmap.

Create transparent demand management, delivery, and value realization practices that give leaders a clear view of progress, tradeoffs, risks, and results.

Grow People. Build Leaders. Represent Data and AI.

Mentor and coach team members through regular one on ones, direct and timely feedback, meaningful recognition, and individualized development plans.

Build clear career paths for data engineers, AI engineers, architects, solutions engineers, and product leads across technical, product, and management tracks.

Sponsor team members for stretch assignments, certifications, publications, conference presentations, and advancement opportunities. Develop successors and a strong bench of future managers, principal architects, and senior product leaders.

Create a culture of psychological safety, curiosity, inclusion, accountability, and continuous learning where concerns are raised early and teams learn from mistakes.

Teach the team to explain tradeoffs and communicate effectively with clinicians, scientists, operational leaders, executives, and board committees.

Represent data and AI to executive leaders, governance bodies, external collaborators, peer institutions, academic partners, and technology vendors. Translate complex topics into mission and business terms using stories, visuals, and measures that matter.

What You Bring

• Minimum Education: Bachelor's degree in business administration, computer science, data science, information science or related field required.
• Master's degree preferred.
• Minimum Experienc e: Minimum requirement: Ten (10) significant years experience in an IT functional area with experience managing mid-large teams with focus on both achievement of objectives as well as critical skill-building.
• Experience having led large-scale projects/processes through to implementation.
• Experience with operational mgmt., budget planning & vendors and resource mgmt.
• Prior experience in building external connections with peers, emerging technology communities, and vendor networks.
• Proven performance in earlier role/comparable role.
• 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 is highly preferred.
• Demonstrated success leading enterprise data platform implementation and adoption with measurable growth in users, use cases, certified data products, and mission or business outcomes highly preferred.
• Experience leading blended teams across engineering, architecture, product management, platform operations, and solutions delivery highly preferred.
• Experience establishing enablement programs, federated or hub and spoke operating models, and data champion networks highly preferred.
• Experience moving machine learning or generative AI solutions from pilot to reliable production operation highly preferred.
• Experience managing vendors, consulting partners, contracts, budgets, and cloud investments highly preferred.
• Experience in healthcare, life sciences, biomedical research, an academic medical center, pediatric medicine, or oncology highly preferred.
• Experience with enterprise systems such as Epic, Workday, Adaptive Planning, Visier, HR and workforce management, CRM, ServiceNow, and financial planning platforms highly preferred.
• Databricks or Microsoft Azure certifications highly preferred.
• Knowledge, Skills, and Abilities: 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, 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.
• AI ready data product delivery, including semantic layers, metadata, lineage, data contracts, data quality, structured and unstructured data preparation, embeddings, retrieval, and knowledge grounding.
• MLOps and LLMOps, including evaluation, monitoring, versioning, guardrails, human oversight, incident response, and lifecycle management.
• Experience operating data and analytics as a product practice.
• Data governance implementation in a modern platform, including cataloging, ownership, stewardship, classification, access control, lineage, quality, and responsible AI controls.
• 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 tracking emerging data, platform, and AI capabilities, translating them into a sequenced roadmap of what to enable next, and advising executive leaders on investment and direction.
• Strong executive presence and presentation skills, with the ability to explain complex technical concepts in clear, plain language to senior executives, boards, clinicians, scientists, and external audiences.
• Excellent written and verbal communication, stakeholder engagement, emotional intelligence, sound judgment, and the ability to influence across clinical, scientific, administrative, operational, and technical groups.
• Experience speaking at conferences, publishing, or representing an organization in external data and AI collaborations.

Preferred Qualifications:

• 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.
• Why Join St. Jude?
• You will work on data that matters in service of a mission unlike any other. You will have the backing of institutional leaders who see data and AI as essential to St. Jude’s future and the opportunity to build a team and platform that people across the institution rely on to advance cures and save children.