• Leverage complex datasets to generate actionable insights that influence payments & fraud strategy and drive business outcomes. • Support development and testing of payment and fraud rules: help define objectives, use data to inform and optimize rules, analyze A/B tests, and measure impact on key metrics. • Design, build, and maintain dashboards and reporting solutions to enable self-service analytics across the organization. • Collaborate with product, engineering, marketing, finance, and other teams to scope analytical projects and deliver insights. • Investigate anomalies and support root cause analysis for payment & fraud incidents. • Communicate complex findings clearly and effectively through presentations, reports, and data storytelling tailored to various audiences. • Advocate for data quality, governance, and best practices to ensure consistency, reliability, and security of data assets. • Mentor junior analysts and interns and contribute to the growth of the broader analytics community within the organization. • Participate in weekly payments syncs, sprint planning, and cross-team channels to stay aligned on priorities and incident response. • Independently identify high-impact business problems and develop the right data-driven solutions.

• Collaborate with engineers, logisticians and product managers to understand requirements, gather supporting information specifications and develop readiness based sparing model. • Contribute technical expertise in data needed and structure to develop and model the simulation to identify the most effective option on the readiness cost curve. • Participate in planning sessions to acquire detailed information develop data pipeline architecture that align with project requirements. • Extract supporting data from identified tools, review available supportability reports and engineering details to assess quality of data prior to developing a readiness at cost model. • Document data gathering processes, pipeline configurations, and data flows to ensure consistency and future support. • Ensure accuracy and completeness of all documentation. • Foster effective collaboration with cross-functional teams to achieve project objectives. • Communicate complex technical information clearly and effectively. • Utilize identified software and follow business rules and processes. • Identify areas to improve efficiency and accuracy and integrate those into modeling best practices

• Lead advanced data analytics, statistical modeling, and predictive analysis initiatives to support readiness based sparing models to support decision-making and strategic planning. • Partner with senior stakeholders to define analytical objectives, shape problem statements, and translate business challenges into actionable, data-driven strategies. • Enforce, maintain and contribute process improvements to documented enterprise data definitions, business rules, governance frameworks, and data quality standards. • Provide technical leadership and mentorship to junior analysts, supporting skill development and analytical best practices. • Serve as a trusted advisor and subject matter expert on data analytics methodologies, tools, and advanced analytical techniques. • Contribute technical expertise in data needed and structure to develop and model the simulation to identify the most effective option on the readiness cost curve. • Participate in planning sessions to acquire detailed information develop data pipeline architecture that align with project requirements. • Extract supporting data from identified tools, review available supportability reports and engineering details to assess quality of data prior to developing a readiness at cost model. • Document data gathering processes, pipeline configurations, and data flows to ensure consistency and future support. • Identify, define, and lead continuous improvement initiatives for analytics processes, data workflows, and system performance. • Ensure accuracy and completeness of all documentation. • Foster effective collaboration with cross-functional teams to achieve project objectives. • Communicate complex technical information clearly and effectively. • Utilize identified software and follow business rules and processes. • Identify areas to improve efficiency and accuracy and integrate those into modeling best practices.

• Partner with Clinical Transformation leadership and the Chief Population Health Officer to define core Clinical Services performance metrics across productivity, quality, ROI, clinical impact, and operational efficiency. • Design scalable, standardized reporting frameworks aligned with centralized analytics standards. • Develop KPI hierarchies spanning individual, team, service-line, and executive-level performance. • Ensure metrics align with financial performance, client outcomes, and overall service value. • Build and maintain dashboards and reporting tools that provide real-time operational visibility. • Partner with centralized Analytics and Data Engineering to ensure clean data pipelines and scalable architecture. • Reduce manual reporting through automation and systematization. • Create standardized reporting packages for internal leadership and customer-facing needs. • Develop standardized, scalable client reporting frameworks. • Support Services leadership in translating analytics into clear, client-ready narratives. • Ensure reporting is consistent, defensible, and automated wherever possible. • Identify opportunities to productize analytics deliverables. • Act as embedded liaison between Clinical Services and centralized Analytics teams. • Ensure alignment on metric definitions, governance, and data standards. • Collaborate with Product and Engineering when service workflows require new data capture or reporting capabilities. • Support analytics needs during new service launches and workflow redesigns. • Identify trends, bottlenecks, and optimization opportunities across service delivery. • Conduct deeper analyses to inform staffing models, pricing strategy, and operational efficiency. • Support scenario modeling and capacity forecasting. • Help build and evolve a data-driven operating model for Clinical Services.