Director, Data and AI

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 Use

• 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.