• Analyze the AI Agentic framework (initially focused on Relay, with potential scope for Midgard and other applications) to determine what should be deployed, newly built, or adapted for specific accounts and use cases. • Identify process gaps and areas that can be optimized with AI tooling; collaborate with product and development teams to design new tools and agentic workflows. • Conduct investigations and analysis of failed AI prompts to identify root causes and improvements. • Pilot new AI agents for upcoming programs, test outputs, and optimize prompts to improve results. • Monitor production daily and track the status of deployed AI agents. • Identify gaps and propose enhancements to strengthen overall AI-enabled workflows. • Collaborate closely with the development team to iterate, refine, and implement improvements. • Improve prompts for AI agents based on agent results and ongoing analysis of system health. • Map and analyze existing workflows across business units, identifying inefficiencies, inconsistencies, and edge cases. • Develop and maintain clear, detailed documentation—process maps, SOPs, functional requirements—suited for both business and technical stakeholders. • Identify opportunities for standardization and automation, working with engineering and product teams to define what should be automated, how, and why. • Support the development of logic and structured inputs for intelligent systems (including rule-based and language model-based automations). • Leverage SQL and other data tools to validate assumptions, quantify opportunities, and measure the impact of changes. • Collaborate across functional teams to ensure new processes are both operationally sound and aligned with data architecture and systems. • Contribute to maturing the overall process discipline within the org by introducing structured methods and mentoring others informally through collaboration.

• Acting as the primary conduit between legal/regulatory findings and the Core Network team, translating regional mandates into clear functional requirements. • Comparing local regulatory requirements against existing platform capabilities and collaborating with Core Engineers to identify necessary enhancements. • Creating high-level technical workflows and flowcharts for regulatory services (e.g., MNP, Lawful Interception) to guide engineering teams. • Defining business rules and data exchange requirements for Number Portability (MNP) and specifying functional needs for voice/SMS routing. • Defining data points and reporting frequencies required by local authorities, ensuring the engineering team builds the necessary data extraction tools. • Leading the definition of acceptance criteria to ensure that technical delivery successfully satisfies the specific regulatory hurdles of each country. • Managing the technical-regulatory checklist for various countries simultaneously, ensuring all dependencies are captured for the development sprint. • Coordinating with local central databases and external partners to ensure interworking specifications meet local quality-of-service mandates.

• Develop and manage process improvement initiatives for member and providers • Conduct, collect and analyze information from office site and/or medical record reviews • Monitor changes, corrections and clarifications in applicable regulatory/accrediting body requirements • Conduct continuing education to providers on STARS, HEDIS, and HCC Coding

• Supporting the CVS Health PBM Underwriting team by completing quality audits of financial documentation outlined in the contracts of CVS Health customers. • Aggregating discrepancies, identifying trends, and reporting findings to Underwriting senior leaders. • Ensuring adherence to underwriting guidelines, regulatory requirements, and risk appetite while continuously improving performance through actionable insights and data analysis. • Leveraging data to identify trends and opportunities for continuous improvement within the quality assurance (QA) framework for underwriting practices. • Designing and maintaining data-driven KPIs and quality scorecards to monitor underwriting performance, consistency, and compliance. • Analyzing underwriting data to detect patterns of procedural errors and developing actionable solutions. • Collaborating with underwriting, data science, and audit teams to implement quality improvement initiatives. • Driving automation and reporting tools to streamline quality reviews and enhance data transparency. • Presenting quality insights, metrics, and recommendations to senior leadership in a clear, data-supported format.