• Design and deliver training that strengthens risk adjustment knowledge, elevates coding accuracy, and drives quality across the organization. • Collaborate with operations and leadership to identify learning needs and create impactful educational programs. • Support employees from onboarding through ongoing development, ensuring they have the tools to succeed. • Partner with external vendors to ensure compliance standards are met. • Develop, maintain, and communicate coding guidelines that align with CMS regulations. • Responsible for department wide risk adjustment quality assurance.

• Stand up and continuously improve an enterprise SharePoint Online repository for governance documents, including information architecture, metadata/taxonomy, content types, versioning, permissions, retention, search, and workflow automation • Define and socialize the governance documentation framework (scope, definitions, document types, naming conventions, approval paths, lifecycle controls) • Partner with across Western Digital to identify required policies and process documentation, and to aggregate and organize existing governance artifacts (guidelines, procedures, standards, charters, playbooks) • Work with document owners/SMEs to draft or update policies and procedures, ensure clarity and consistency, and apply WD branding and numbering standards • Guide owners through approvals; manage publishing, review cycles, and controlled updates on defined cadence (e.g., annual/biannual), maintaining audit‑ready evidence and traceability • Establish controls and documentation that meet internal audit, SOX, ISO/QMS, data governance, and security requirements • Create training, quick guides, and communications; host clinics for authors/approvers; champion adoption across sites and functions • Define KPIs and dashboards (e.g., % coverage of policy inventory, cycle time for approvals, % docs current vs. expired, repository usage/adoption, audit issues remediated)

• Identify, investigate, and mitigate fraudulent purchase and deposit activity • Implement logic targeting bad actors and high-risk behavior associated with unauthorized purchasing activity across multiple regions • Measure fraud performance across payment methods and develop core metrics • Proactively identify and close gaps in our monitoring • Own risk controls from end-to-end and scale processes to provide appropriate coverage as we expand our business • Own reporting processes and communication with relevant banking partners • Work with internal stakeholders in Product, Growth, Data Science and Engineering to develop and drive forward custom risk management strategies and techniques for different geographies • Use data to assess risk and make decisions impacting large segments of our customer population; effectively measure and assess associated tradeoffs using quantitative analysis • Synthesize data learnings into compelling stories and communicate them to Payments Risk leadership

• Define the ML data and feature strategy for fraud detection, determining what data needs to enter our systems so our models can take intelligent, high-accuracy action on a small fraction of traffic where intervention matters most. • Own the end-to-end feature engineering pipeline identifying, building, validating and promoting features that drive measurable improvements in ATO and scam ML performance. • Diagnose gaps between current tooling infrastructure and the solutions needed, and drive the roadmap to close them leveraging your understanding of how the industry has evolved to make the right architectural calls. • Partner with Machine Learning Engineers to translate analytical insights into production-ready ML systems, ensuring models are instrumented, monitored, and continuously improved. • Set technical direction for the ML Analytics function within Growth & Risk, mentoring junior team members who need a senior practitioner to define the approach and translate direction into execution. • Partner cross-functionally with Product Managers and Risk analysts to surface fraud signals and translate ML findings into business-impacting decisions. • Serve as the team's institutional knowledge resource on ML industry evolution — helping the organization understand why certain solutions work, what historical architectural decisions mean for current tooling, and where the industry is headed next.