Vice President, Data & AI
Listed on 2026-07-26
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IT/Tech
Data Engineering, AI Engineer (Applied/Software), Data Science Manager, AI Business & Operations
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.
- 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.
- 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.
- 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…
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