Director of AI, Payment & Revenue Integrity

Job Summary

The Director of AI, Payment & Revenue Integrity leads the design, delivery, and scaling of artificial intelligence and automation capabilities across Claritev’s payment integrity and revenue integrity portfolio. This role owns the translation of claims, contract, and clinical data into production AI systems that identify overpayments, price out-of-network and contracted claims accurately, and automate adjudication support at enterprise scale.

The role sits at the intersection of deep domain expertise and applied AI. It requires a leader who can move fluidly between model architecture decisions, payment policy interpretation, operational workflow design, and executive and client conversations — and who is accountable for the financial return of the portfolio, not only its technical delivery.

The ideal candidate brings a substantial record of delivering AI and automation outcomes inside a healthcare payment integrity environment, including production generative and agentic AI systems, intelligent document processing across medical and contractual documents, and quantified, auditable business results. Experience building teams and operating as a peer to business and operations leadership is essential.

Roles & Responsibilities

Strategic Leadership

• Set the AI and automation strategy for payment and revenue integrity, and own the multi-year roadmap across data mining, claims pricing, contract adjudication, and overpayment identification.
• Translate business opportunity into a prioritized technical portfolio, with sizing and return expectations defensible to executive leadership and Finance.
• Serve as the accountable owner for portfolio outcomes — incremental revenue, cost avoided, hours returned, and accuracy gained — reported with the rigor applied to any other enterprise investment.

AI Solution Delivery

• Move agentic and multi-agent workflows from proof of concept into governed production, including claims pricing agents that ingest claims, apply contractual and out-of-network logic, reprice, and transmit to downstream pricing systems.
• Own the intelligent document processing estate across UB-04, CMS-1500, medical records, client policies, provider contracts, and legal files, holding classification, extraction, and PHI/PII handling to audit-grade standards.
• Build and scale the extraction of adjudication logic from contract and policy documentation into executable rules and deployable code.
• Direct the modernization of the automation estate toward code-first approaches, reducing legacy RPA licensing dependency without disrupting production revenue.

Payment Integrity Content & Automation

• Establish a repeatable concept-generation engine that pairs contract-derived rules with outlier, time-series, and duplicate detection to produce new automated concepts continuously.
• Apply domain judgment across No Surprises Act and IDR workflows, drug pricing methodologies, coordination of benefits, clinical coding, and reference-based and out-of-network pricing.
• Partner with operations to embed AI output into auditor and analyst workflows so that accuracy and productivity gains are realized, not merely demonstrated.

Cross-Functional Engagement

• Partner with Payment Integrity Operations, Clinical, Product, Legal, Compliance, and Responsible AI to ensure every deployment is defensible on regulatory and model-governance grounds.
• Communicate technical outcomes in clear commercial terms for business, executive, and client stakeholders.
• Represent Claritev externally in client executive briefings, industry forums, and published thought leadership on AI in payment integrity.

Team & Capability Building

• Hire, develop, and retain a team spanning applied AI engineering, data science, and payment integrity subject matter expertise.
• Establish the operating rhythm, intake process, and delivery standards for the function.
• Build AI literacy across partner organizations through enablement, documentation, and knowledge management.

Candidate Profile

• Ten or more years in healthcare payment integrity, claims, or revenue integrity, with progressive leadership responsibility.
• Demonstrated ownership of AI/ML and automation programs delivering quantified, auditable business outcomes at scale.
• Hands-on experience deploying generative AI in a regulated healthcare environment on an enterprise LLM platform (Azure OpenAI, AWS Bedrock, Google Vertex, or comparable).
• Practical experience with agentic or multi-agent architectures applied to operational workflows, with sound judgment about where they are and are not appropriate.
• Working fluency in claims adjudication logic, contract interpretation, out-of-network and reference-based pricing, overpayment and outlier concept development, and payment policy.
• Document AI experience across standard healthcare forms and unstructured clinical and contractual documents, including PHI/PII identification.
• Python fluency sufficient to review the team’s work credibly and to make build-versus-buy determinations independently.
• Experience standing up an in-house AI platform or capability serving both internal teams and clients (preferred).
• Experience migrating RPA workloads to code-first automation with quantified savings (preferred).
• Participation in enterprise AI governance — steering committees, model risk review, or responsible AI standards (preferred).
• Bachelor’s degree in a quantitative, technical, or business discipline; executive education in AI product or strategy is valued alongside practitioner experience.
• Exceptional communication and leadership skills, with the ability to influence organizational direction.

Compensation

The salary range for this position is $210K to $230K. Specific compensation offers are determined based on a variety of factors including the candidate’s education, experience, skills, work location, and internal equity considerations In addition to base salary, this position is eligible for an annual performance bonus and a comprehensive benefits package, including health insurance and a 401(k) retirement plan

#LI-MC2