Vice President, Data & AI

Position Description

At UNOS, the data we steward does not just power analytics products. It supports the systems, insights, and decisions that help save lives through organ donation and transplantation.

The Vice President, Data & AI is a senior technology executive responsible for defining and executing UNOS's enterprise data and artificial intelligence strategy. This role provides leadership across Data Engineering, Analytics Engineering, Data Architecture, Data Governance, Data Products, and AI/ML Engineering, ensuring these capabilities operate as a unified function that advances UNOS's mission and strategic priorities.

The Vice President serves as the executive sponsor for enterprise data and AI initiatives, driving modernization of UNOS's data ecosystem while building future capabilities that leverage advanced analytics to build intelligent products and tools at scale and new data integration pathways, machine learning, and artificial intelligence to improve organizational performance, customer value, and decision-making.

Reporting directly to the Chief Executive Officer, this role serves as a member of the Technology Leadership Team and works closely with Executive Leadership to align Data & AI investments with organizational priorities and long-term strategy.

Key Responsibilities

Strategic Leadership & Vision

• Define and execute the enterprise Data & AI strategy, establishing a multi-year roadmap aligned with organizational goals, technology priorities, and mission outcomes.
• Serve as the executive champion for data as a strategic enterprise asset, promoting practices that improve data quality, accessibility, trust, and business value.
• Partner with Executive Leadership to align Data & AI investments with organizational priorities, product strategy, operational excellence, and future growth opportunities.
• Advise leaders on emerging trends in healthcare data, interoperability, analytics, artificial intelligence, and technology innovation.
• Establish performance measures that demonstrate the business impact and value realized through Data & AI initiatives.
• Provide leadership through Directors, Managers, and senior technical leaders across the Data & AI organization.

Organizational Leadership

• Lead and develop a high-performing organization spanning Data Engineering, Analytics Engineering, Data Architecture, Data Governance, Data Products, and AI/ML Engineering.
• Establish organizational structures, workforce plans, succession strategies, and leadership development programs that support long-term business needs.
• Foster a culture of accountability, innovation, collaboration, continuous improvement, and technical excellence.
• Allocate resources across multiple functions to balance operational priorities, modernization efforts, innovation, and strategic initiatives.
• Develop leadership capability throughout the organization and ensure effective management practices at all levels.

Data Platform & Analytics Strategy

• Lead modernization of UNOS's enterprise data platform through scalable, cloud-native architecture and data engineering practices.
• Oversee the design, implementation, and governance of enterprise data infrastructure, including data lakes, data warehouses, semantic models, and curated analytical datasets.
• Establish standards for reliability, scalability, observability, security, performance, and maintainability.
• Ensure mission-critical data assets and analytical platforms effectively support operational, scientific, research, and customer-facing needs.
• Guide platform strategy, architecture decisions, and technology investments that support future organizational growth and innovation.

AI, Data Products & Innovation

• Define and lead UNOS's artificial intelligence and machine learning strategy, ensuring alignment with business objectives, customer needs, and regulatory requirements.
• Build and mature AI/ML capabilities, including technology, governance, processes, and talent required to develop and operationalize AI solutions at scale.
• Establish standards and oversight for responsible AI, including transparency, explainability, governance, monitoring, and risk management.
• Evaluate and guide the use of machine learning, predictive analytics, generative AI, and emerging technologies across internal and customer-facing solutions.
• Partner with Product, Research, Technology, and business leaders to identify opportunities for data-driven innovation and new capabilities.
• Champion a data product mindset that treats enterprise data assets as strategic products with defined ownership, quality standards, and customer expectations.

Data Governance, Quality & Compliance

• Serve as the executive authority for enterprise data governance, data stewardship, data quality, and AI oversight.
• Establish organizational policies, standards, and governance frameworks for data management, privacy, security, retention, accessibility, and responsible AI usage.
• Sponsor governance forums that prioritize investments, manage risk, establish accountability, and support enterprise decision making related to data and AI.
• Ensure compliance with HIPAA, data privacy requirements, information security standards, and emerging AI governance expectations.
• Promote privacy-by-design, security-by-design, and high-quality data management practices across the organization.

Financial & External Leadership

• Own the Data & AI operating budget, including workforce planning, technology investments, vendor management, and long-term capability development.
• Develop and oversee multi-year investment roadmaps supporting data, analytics, governance, and artificial intelligence capabilities.
• Establish value realization measures and prioritize investments to maximize organizational impact and return on investment.
• Lead strategic vendor relationships and technology partnerships supporting the Data & AI ecosystem.
• Represent UNOS with healthcare partners, researchers, government agencies, technology vendors, and industry groups on matters related to data, analytics, interoperability, and artificial intelligence.

Minimum Requirements

• 15+ years of progressive experience in data, analytics, technology, engineering, artificial intelligence, machine learning, or related disciplines.
• 7+ years of experience leading managers, senior technical leaders, and multi-disciplinary organizations.

Critical Skills

• Demonstrated success leading enterprise-scale data modernization, analytics, governance, cloud transformation, or AI initiatives.
• Experience managing significant technology investments, vendor relationships, and complex organizational initiatives.
• Proven ability to influence executive leadership and drive strategy through data, analytics, and technology capabilities.
• Experience building and leading organizations across multiple technical disciplines, including engineering, architecture, analytics, governance, and AI/ML functions.
• Expertise with modern cloud data platforms including Azure, Databricks, Azure Data Lake, Azure Synapse, Azure Data Factory, and related technologies.
• Strong understanding of data architecture, data modeling, data warehousing, ELT/ETL design, metadata management, and enterprise data operations.
• Experience building and leading large-scale data and analytics platforms supporting both operational and customer-facing solutions.
• Strong knowledge of AI/ML technologies, MLOps, model governance, model deployment, and large language model technologies.
• Deep understanding of data governance, data stewardship, privacy requirements, HIPAA, security controls, and responsible AI practices.
• Ability to establish governance frameworks, operating models, and organizational strategies that improve accountability, scalability, and business value.
• Exceptional executive communication, stakeholder management, and influence skills.
• Ability to translate complex technical concepts into business-focused recommendations for executive and board-level audiences.
• Familiarity with healthcare interoperability standards including HL7v2, FHIR, QHIN, or similar healthcare data exchange models.
• Experience supporting clinical, scientific, research, or highly regulated data environments preferred.

Education

• Bachelor's degree in computer science, Data Science, Information Systems, Engineering, Analytics, Artificial Intelligence, Healthcare Informatics, or a related field required.
  
  
  
  • Master's degree in a related discipline preferred.
  
  
  
  
• Formal coursework or concentration in data architecture, distributed systems, cloud computing, machine learning, or AI is a plus.