VP of Product Management, Risk & Quality AI Solutions

Vatica Health and Cozeva have combined to create a single platform that unites clinician-enabled risk adjustment and clinical documentation with population-health and quality analytics across the payer and provider ecosystem. This is a defining moment: we are building an AI-native organization that improves the accuracy of risk adjustment, closes quality gaps, and reduces clinician burden — driving better outcomes for patients and measurable value for our clients.

The Vice President, Product Management – Risk & Quality AI Solutions will own the roadmap, and delivery of our AI-native Risk & Quality product suite. This is a product leadership role for someone who can set annual strategy, operationalize AI/ML, agentic, and LLM-driven workflows in a regulated healthcare environment, and champion responsible AI as a first-class deliverable. You will report to the SVP, Product Management & AI and partner closely with Engineering, the ML/AI team, Clinical, and Compliance.

This role owns product management for the Risk & Quality AI Solutions portfolio — product strategy, roadmap, delivery, and AI solution performance. It does not include ownership of the underlying data platform or infrastructure, which sits with our Data organization, though you will partner deeply with that team to ensure your products are built on reliable, well-governed data.

Responsibilities

Strategy & Portfolio Ownership

• Own the annual roadmap for the Risk & Quality AI Solutions portfolio, aligning it to company priorities and to opportunities for new revenue, efficiency, and enhanced client value.
• Partner with business stakeholders and the ML/AI team to identify and prioritize the highest-impact AI-native product bets across the combined Vatica and Cozeva product lines.

AI-Native Product Delivery

• Design and deliver AI-native solutions that apply agents, OCR, NLP, and LLMs to automate clinical and operational workflows while maintaining the accuracy standards required in a regulated healthcare setting.
• Own the AI solution lifecycle end to end — problem framing, build, evaluation, production monitoring, and retirement — in close partnership with Engineering and ML/AI.
• Define and scale human-in-the-loop review for use cases such as chart abstraction, summarization, and coding assistance, balancing automation with clinical and compliance safeguards.

Performance, Evaluation & Cost

• Establish evaluation and monitoring frameworks for LLM- and NLP-based workflows, using rigorous measurement methodologies (e.g., precision/recall, false positive/negative analysis) and continuous performance monitoring.
• Define and track KPIs for each product and the broader portfolio, tying them directly to client impact and efficiency gains.
• Govern AI-related costs and evaluate model and engineering effectiveness to ensure a sustainable, scalable solution suite.

Responsible AI Governance

• Serve as a leader in responsible-AI policy and practice for the Risk & Quality portfolio, ensuring solutions are transparent, auditable, and compliant with applicable healthcare regulations. (Note: verify the specific frameworks and obligations that apply to your products with Compliance.)

Team Leadership

• Mentor a high-performing product management team; implement OKRs and drive consistent, predictable delivery.

Requirements

• 10+ years in data- or AI-oriented product management, including a track record of building and launching Risk & Quality solutions powered by ML/AI.
• Demonstrated success delivering mission-critical, regulated software products at scale, and experience building and leading product teams.
• Proven experience operationalizing AI- or data-driven products in production, including evaluation and monitoring frameworks for LLM or NLP-based workflows.
• Expertise in measurement and evaluation methodologies (precision/recall, false positives/negatives) and continuous performance monitoring.
• Strong technical fluency — able to work closely with Engineering on system architecture, data pipelines, API design, and integration patterns.
• Experience working within an AI development lifecycle (AI DLC) — from data and model development through evaluation, deployment, monitoring, and iteration.
• Experience leading or operating in blended offshore/onshore teams, including managing delivery across time zones and distributed collaboration.
• Experience working in cross-functional product/AI/engineering pods, with the ability to align product, data science, and engineering toward shared outcomes.
• Working knowledge of Medicare Advantage, HCC coding, and CMS risk adjustment models, including the