FBS – Farmer Business Services is part of Farmers operations with the purpose of building a global approach to identifying, recruiting, hiring, and retaining top talent. By combining international reach with US expertise, we build diverse and high-performing teams that are equipped to thrive in today’s competitive marketplace. We believe that the foundation of every successful business lies in having the right people with the right skills. That is where we come in—helping Farmers build a winning team that delivers consistent and sustainable results. Since we don’t have a local legal entity, we’ve partnered with Capgemini, which acts as the Employer of Record. Capgemini is responsible for managing local payroll and benefits. **What to expect on your journey with us:** - A solid and innovative company with a strong market presence - A dynamic, diverse, and multicultural work environment - Leaders with deep market knowledge and strategic vision - Continuous learning and development Objective: To support the numerous in-flight and upcoming business initiatives Farmers needs a dedicated support team to manage our customer and product analytics tools, Segment, Mixpanel, and Salesforce Data360 (formerly Data Cloud). This team will operate as cross functional center of excellence building and supporting a Customer Events Hub focused on delivering real-time event streaming and product analytics capabilities for business partners.

• Design and own **business-critical metrics** (definitions, edge cases, documentation) • Build **clean, reusable SQL models** (transformation layer) that become source-of-truth datasets • Partner with stakeholders to turn ambiguous questions into clear analytical deliverables • Create self-serve datasets and lightweight reporting so teams can answer questions without bottlenecks • Improve data reliability: sanity checks, reconciliations, and data quality monitoring (as available) • Contribute to analytics best practices (naming, documentation, metric governance)

• 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 and maintain robust data transformations and mappings to convert raw medical claims into standardized canonical models. • Perform detailed data quality audits to detect, troubleshoot, and resolve issues impacting accuracy and completeness. • Write and optimize Spark SQL queries to validate data pipelines and support ongoing analytics. • Document data transformation logic and lineage to ensure transparency and reusability. • Collaborate cross-functionally with business stakeholders to translate data needs into technical specs. • Support onboarding of new client data feeds, ensuring smooth integration with minimal disruption. • Partner with data engineers to build and scale data pipelines, improving performance and reliability. • Act as a subject matter expert for healthcare claims data—advising on risk, trends, and business impact. • Work closely with data scientists to enable advanced analytics, ML models, and reporting use cases. • Troubleshoot ETL workflows and performance issues, driving continuous improvement in data architecture.