VP, Data/AI Platform

Highlights

• Location Remote, US
• Start Date Nov 2, 2026
• Work Location Allows Remote Work

Skills

• Data Platform
• Data Engineering
• Artificial Intelligence
• AWS
• Snowflake
• Strategy
• Leadership
• HealthTech

Description About MatrixCare MatrixCare provides software solutions to more than 15,000 providers supporting skilled nursing, senior living and long-term care, life plan communities, and home health and hospice care. A multi-year winner of the Best in KLAS award, MatrixCare is trusted by thousands of facility-based and home-based care organizations to improve provider efficiencies and promote a better quality of life for the people they serve. For more information, visit www.matrixcare.com.

About The Role The VP, Data/AI Platform owns the enterprise data platform, the artificial intelligence and machine learning agenda, and the shared platform services every product team builds on. As Vice President, you set the strategy for how we turn our most valuable asset, data, into a competitive advantage leveraging AI.

You lead a multidisciplinary organization spanning data engineering, data science and machine learning across the United States and India. You are accountable to the Chief Technology Officer and to the executive leadership team for the reliability and economics of the data platform, for moving artificial intelligence capability from idea into production at a pace the market rewards, and for the governance that makes all of it defensible to customers, auditors, and regulators.

What You’ll Do

• Own the enterprise data and artificial intelligence strategy, and translate it into a funded, sequenced roadmap that the executive leadership team and the board can hold you to.Own the target architecture for the data platform, including the cloud data platform, streaming and batch ingestion, the semantic and consumption layers, and the reliability, quality, and cost posture of the whole estate.
• Deliver data products that customers experience directly: benchmarking, operational and clinical insight, and the analytics that make the clinical and financial performance of a provider organization visible and actionable.
• Own the artificial intelligence and machine learning portfolio end to end, from problem selection and model development through evaluation, human in the loop design, production deployment, monitoring, and the measured business outcome each capability is meant to produce.
• Select and stand up the orchestration, tooling, and machine learning operations platform that agentic and generative capability runs on, and hold it to enterprise reliability and cost standards.
• Establish and run artificial intelligence governance: model risk management, data classification, evaluation and guardrail standards, bias and drift monitoring, and the audit evidence that regulated customers expect before they will buy.
• Own shared platform services and the internal developer platform, so product engineering teams ship faster on common capability rather than rebuilding it team by team.
• Own data governance, master data and patient identity, and interoperability of the data layer, so that a patient, a facility, and an episode mean the same thing everywhere in the company.
• Partner with the VP, Product Management and the commercial organization to convert platform capability into packaged, priced offerings, and to choose investment from customer and usage evidence rather than from opinion.
• Own the build, buy, partner, or license decision for artificial intelligence capability, with the sizing, governance, and contractual analysis to support it.
• Manage the domain budget and vendor portfolio, including cloud and data platform spend, with a clear view of unit economics and a bias toward capability that compounds rather than capability that is rented indefinitely.
• Recruit, develop, and retain a high performing, diverse team across distributed locations. Conduct regular one to ones and performance conversations, build succession beneath every leadership seat, and grow the technical bench rather than concentrating expertise in a few people.

What You’ll Bring Required Qualifications:

• Bachelor’s degree required. Advanced degree in a quantitative, computer science, or business discipline preferred.
• 12 or more years of relevant experience across data platform, data engineering, data science, and machine learning, including 5 or more years leading leaders….