• Ability to learn and improve Keller Postman’s data integrity processes across the lifetime of a legal case. • Maintain a keen and dynamic understanding of legal and financial relationships to the firm toward the end of validating the involvement of all relevant parties prior to settlement. • Collaborate with stakeholders to understand business needs and gather detailed functional requirements for process improvements, data discrepancy resolution, or efficiency enhancements. • Develop strategic process improvements for existing and emergent applications toward the end of improving data integrity. • Audit and analyze workflows, applications, and systems used across teams. • Develop and maintain processes to reconcile and verify AI-supported claim substantiation and client-submitted data. • Monitor and ensure user and stakeholder compliance with data standards across departments. • Manipulate large datasets to identify and forecast data integrity issues.

• Maintain operational revenue forecast for A&A using proprietary tools and Excel • Compile data from various sources and analyze monthly program activity • Partner with Manager and Finance on month-end activities • Build and maintain relationships with Program Management and Finance

• Must be comfortable interacting with key leadership, articulate, and proactive. • Must be able to work in a fast paced environment to prioritize and complete assigned tasks with minimal guidance, seeking guidance and clarification as required. • Create complex cartographic products on a variety of mediums (i.e. desktop, web based, mobile). • Perform a variety of quality control, quality assurance, and currency of geospatial information produced.

• Conduct in-depth portfolio and trend analysis to identify key performance drivers, emerging risks, and growth opportunities • Develop insights and recommendations to optimize underwriting policies, credit decisioning, and fraud risk strategies • Monitor and evaluate key credit metrics (e.g., approval rates, loss rates, delinquency trends, vintage performance) and communicate findings to stakeholders • Partner with Analytics, Data Science, and Data Engineering to build scalable data models, automated reporting, and repeatable performance monitoring frameworks • Support strategy testing and measurement efforts (e.g., champion/challenger experiments) to improve decision quality and customer outcomes • Translate complex analyses into clear, concise narratives and recommendations for leadership and cross-functional partners • Mentor team members on analytical best practices and help strengthen a data-driven culture across the organization