Revenue-cycle analytics / decision-support / RCM reporting leader

AI Training & Evaluation • Independent Contractor • Remote • Weekly Payouts

Mercor is working with a leading AI research lab to improve the capabilities of next-generation AI systems. We are seeking experienced Revenue Cycle Analytics and Decision-Support leaders, including RCM reporting managers and analytics directors, to evaluate AI tools designed to transform revenue cycle intelligence and financial decision-making. Your expertise in RCM data analytics, financial reporting, and decision-support will directly shape AI systems that deliver actionable insights across the full revenue cycle.

Responsibilities

The Revenue-cycle analytics / decision-support / RCM reporting leader role is a remote contract position. It suits experienced professionals who want flexible, project-based AI work reviewed on a rolling basis. You collaborate directly with client teams on real deliverables that help train and evaluate next-generation AI systems, with no long-term commitment required.

• Lead revenue cycle analytics, reporting, and decision-support functions to drive data-driven performance improvement across RCM operations.
• Evaluate AI-generated revenue cycle analytics outputs, KPI dashboards, and financial modeling recommendations for accuracy and completeness.
• Develop and maintain revenue cycle reporting frameworks covering patient access, coding, billing, denials, A/R, and collections performance.
• Build and interpret dashboards, scorecards, and trend analyses to support operational and executive decision-making.
• Conduct root cause analyses of revenue cycle performance variances and develop data-driven improvement recommendations.
• Collaborate with finance, IT, and operational revenue cycle teams to align analytics infrastructure with strategic priorities.
• Manage RCM data governance, including data definitions, data quality standards, and reporting consistency.
• Support revenue cycle forecasting, budget modelling, and net revenue realisation analysis.
• Annotate AI outputs and provide structured analytical feedback to support AI training datasets.

Requirements

• 5+ years of experience in revenue cycle analytics, RCM reporting, or healthcare financial decision-support, with at least 2 years in a leadership role.
• Deep knowledge of revenue cycle KPIs and financial metrics across patient access, coding, billing, denials, and collections domains.
• Proficiency with healthcare analytics platforms, BI tools (Tableau, Power BI, or equivalent), and SQL-based data analysis.
• Experience with EHR-integrated analytics platforms and RCM reporting systems.
• Strong financial modelling and data interpretation skills with the ability to translate complex data into actionable recommendations.
• Exceptional written and verbal English communication skills.
• High attention to detail with the ability to identify data quality issues and analytical errors in AI-generated outputs.

Preferred Qualifications

• HFMA CRCR, CHFP, or healthcare analytics certification.
• Experience with predictive analytics, revenue cycle forecasting, and net revenue modelling.
• Background in health system, large physician enterprise, or RCM outsourcing analytics operations.
• Familiarity with AI tools and comfort evaluating AI-generated analytics content.
• Experience implementing enterprise-wide revenue cycle analytics infrastructure or BI platform migrations.

Why Join?

• Contribute to the development of frontier AI systems in healthcare.
• Collaborate with a world-class AI research organisation.
• Gain exposure to cutting-edge AI workflows in revenue cycle analytics and decision-support.
• Opportunity to work on high-impact projects shaping the future of healthcare AI.

Details

• Pay: $100 per hour (hourly)
• Commitment: 20 hours per week • remote
• Eligible locations: United States
• Platform: Mercor (weekly payouts via Stripe or Wise)

Submit your application via the link below. Qualified candidates move through Mercor's short AI interview and selection process. Projects can be extended, shortened, or concluded early depending on needs and performance.