• Be a catalyst to help the card business functions operate strategically using data • Drive evaluation of competing priorities across business functions and appropriate disposition solutions that are right-sized and scalable • Drive business growth by identifying new opportunities and hypothesis development using data across Credit Risk, Portfolio management, Fraud and marketing • Independently manage cross-functional relationships across Product, Engineering, Operations, Credit Risk, and executive leadership • Influence stakeholders through clear communication and data-driven recommendations • Develop A/B test designs to implement a robust test and learn strategy • Perform ad-hoc analysis ranging from open exploration data analysis to specific root cause analysis • Analyze Credit Card engagement and credit performance and identify levers and patterns that can increase card usage and utilization • Liaison with Data Engineering and Product to build sustainable and scalable data solutions to address business’ current and future needs • Own and manage BI reports becoming a focal point for business’ data and analytics needs • Leverage AI tools to accelerate analytical velocity and democratize data through self-service agents and tools

• Perform extensive SQL-based data transformations, integrating data from multiple internal and external sources (e.g., claims, media, CRM, EHR-derived datasets). • Define, validate, and govern metrics aligned to pharmaceutical business questions (e.g., brand performance, HCP engagement, patient journeys). • Apply statistical reasoning to analyze trends, identify drivers of performance, and ensure analytical rigor in insights and recommendations. • Maintain and iterate on analytical datasets and workflows as new data is ingested. • Conduct thorough exploratory data analysis (EDA) to uncover patterns, anomalies, and opportunities within complex healthcare datasets. • Interpret and translate analytical findings into clear business insights, tailored for stakeholders across marketing, analytics, strategy, and leadership teams. • Manually scrub, structure, and normalize data as needed to ensure data quality and analytical readiness, particularly for deep-dive analyses. • Prepare datasets to support advanced analytics, including exploratory and explanatory machine learning techniques, ensuring accuracy, consistency, and scalability. • Develop data visualizations and dashboards that clearly communicate insights and support decision-making. • Collaborate cross-functionally with data science, technology, media, strategy, and client-facing teams to align analyses with business needs. • Document methodologies, assumptions, and data limitations to support transparency and reproducibility.

• Design, build, and maintain the data models, dashboards, and reporting frameworks that give the commercial organisation clear, trusted visibility across the full commercial funnel. • Use AI tools as a core part of your analytical workflow — accelerating insight generation, improving output quality, and freeing up capacity for higher-order problem solving. • Run deep-dive analytical projects to surface patterns, anomalies, and opportunities in commercial data, translating findings into clear and actionable recommendations for commercial stakeholders. • Define and track KPIs that measure both business outcomes and the effectiveness of the processes driving them, creating a continuous improvement feedback loop. • Own preparation and follow-through for key internal meetings, strategic reviews, and cross-functional forums — ensuring the right data is in the room and decisions translate into clear next steps. • Build the analytical frameworks that underpin quarterly planning, forecasting, and target-setting, producing outputs commercial leaders can use directly. • Map, challenge, and rebuild core commercial workflows — including pipeline management, forecasting cadences, and sales incentive structures — into cleaner, faster, and more scalable ways of working. • Implement operational improvements end-to-end: not just designing the solution, but driving adoption and measuring impact with rigor. • Identify where AI can fundamentally change how commercial processes work, then build and deploy those solutions directly. • Develop a hands-on understanding of our commercial tech stack, diagnosing where friction, data loss, or missed opportunities exist. • Translate commercial problems into well-defined briefs that Product and Engineering teams can act on, bringing enough technical fluency to be a genuine peer in those conversations.

• Lead analytical efforts to analyze complex data to ensure its integrity and accuracy • Play a key role in driving organizational trust in reporting and analytics • Ensure adherence to regulatory requirements • Collaborate with engineering, data excellence, pricing and analytics, finance, and compliance teams to improve data quality • Proactively query databases, review data and dashboards, understand data flow processes, and support implementation of data initiatives