Vice President, Data & AI

Transcarent is seeking a V P of Data & AI to lead the technology foundation that powers data and artificial intelligence across the company.

This executive will own the strategy, architecture, and execution of Transcarent’s enterprise data and AI platforms: ensuring our teams have trusted, scalable, secure, and modern infrastructure to build intelligent products, power AI-enabled experiences, and put data to work across the organization.

The VP of Data & AI will lead Data Engineering and AI/ML platform capabilities while partnering closely with Product, Engineering, Analytics, Clinical, Security, and business leaders. This leader will build and operate the foundational platforms, data products, infrastructure, and capabilities that enable our company to move faster.

Success in the first 6–12 months will mean establishing a clear Data & AI platform strategy and architecture; improving the reliability, quality, and accessibility of enterprise data; accelerating the path from AI experimentation to production; and building a scalable platform operating model that supports Transcarent’s next phase of growth.

What You’ll Do

Define and lead Transcarent’s Data & AI platform strategy , establishing the architecture, roadmap, standards, investment priorities, and operating model for the foundational capabilities that power data and AI across the company.

Own and evolve the enterprise data platform , ensuring Transcarent has scalable, reliable, secure, and well-governed infrastructure for ingesting, integrating, transforming, storing, and serving data across products and business functions.

Lead Data and AI Engineering , establishing strong engineering practices and building the pipelines, services, data products, and infrastructure required to make trusted data available throughout the organization.

Build the platform for AI at scale , creating the infrastructure, tooling, services, and standards that enable teams to rapidly develop, evaluate, deploy, monitor , and improve machine learning, generative AI, agentic, and other AI-powered capabilities.

Accelerate the path from AI experimentation to production , reducing friction between promising prototypes and scalable, secure, reliable capabilities embedded in Transcarent products and workflows.

Establish reusable Data & AI capabilities , enabling Product and Engineering teams to leverage common data services, models, APIs, AI services, tooling, and infrastructure rather than creating fragmented solutions independently.

Partner closely with Analytics leadership , ensuring analytics teams have reliable, accessible, well-modeled, and appropriately governed data while maintaining clear ownership between platform/data engineering and analytics.

Establish mature enterprise data governance , including data quality, lineage, metadata, definitions, access, interoperability, lifecycle management, and stewardship in partnership with Analytics, Security, Privacy, Legal, and business stakeholders.

Drive platform reliability and operational excellence , establishing clear standards and measures for performance, availability, scalability, observability, cost efficiency, developer experience, and technical debt.

Build and lead a world-class Data & AI Platforms organization , developing strong leaders and technical talent while creating a culture of ownership, experimentation, engineering excellence, speed, and continuous improvement.

What We’re Looking For

Significant leadership experience building and scaling data engineering, data platform, A I /ML , or related technology organizations in complex, high-growth environments.

Proven experience defining and executing a modern enterprise data platform strategy, including architecture, data engineering, infrastructure, governance, and platform operations.

Deep understanding of modern cloud data architectures, data pipelines, data modeling, distributed systems, APIs, data products, and the technologies used to operate data platforms at scale.

Strong understanding of modern AI architecture and infrastructure, including ML platforms, LLM s, generative AI, agentic systems, model deployment, evaluation, observability, and emerging AI development patterns.

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