ED, Decision Science, Renal TA and US Portfolio Product Strategy

Role Summary

Role: ED, Decision Science, Renal TA and US Portfolio Product Strategy.

The Decision Science, Product Lead is a strategic leader responsible for transforming complex data into actionable insights that drive brand success. This role leads a high-performing IDS POD at the product level and acts as the connector between IDS and the product team, supporting continuous insight generation and strategic alignment across the product lifecycle. Reporting to the VP, Decision Science TA and Launch, the role enables proactive and integrated insight-driven decision making for the brand, addressing business problems, shaping strategy and implementing decision science capabilities through IDS.

Responsibilities

• Shape the strategic direction of the brand by leveraging insights to identify opportunities, address challenges, and optimize performance
• Integrate and synthesize insights from across IDS functions to deliver comprehensive, actionable recommendations that address key brand objectives
• Actively participate in IPST discussions and decision-making, contributing data-driven insights and challenging established thinking to refine strategies
• Understand and help craft the brand narrative for IPST meetings and conversations with senior leadership
• Serve as the primary point of contact and proactive strategic thought partner for the Integrated Product Strategy Team (IPST) and General Management (GM), driving brand performance narrative, and ensuring consistent communication and alignment and collaborate with the IPST Lead to further refine analyses & sharpen insights / recommendations
• Lead IDS POD at product level by working across IDS pillars to direct and synthesize information to help drive brand performance and share key insights with the IPST, while actively pressure-testing and challenging current thinking to drive innovation and informed decision-making
• Support the generation and analysis of insights throughout the entire product lifecycle, ensuring continuous improvement and alignment with brand goals
• Synthesize large volumes of analytics provided by GM, ensuring that insights are translated into actionable strategies for the brand and Pressure-test insights and analyses to ensure robust, high-quality recommendations that drive brand success
• Integrate performance analytics (e.g., diagnose current results with aligned KPIs and drivers, provide clarity on root cause) and predictive analytics (e.g., data-driven scenarios and simulations) to explore/address business issues in the context of the brand
• Lead a team of Brand Performance Managers, fostering a high-performing culture focused on innovation, collaboration, and continuous improvement

Qualifications

• Ten plus years of experience in the pharmaceutical or healthcare industry, with a deep understanding of brand management, product lifecycle management and market dynamics
• A bachelor’s degree in business administration, Life Sciences, Data Science or a related field; an MBA or advanced degree is strongly preferred
• Strong background in therapeutic area strategy, product lifecycle management, and early-stage asset planning, ideally with experience in both established and launch products
• Five plus years of domain expertise in these areas are highly desired: secondary data analytics, data science, primary market research, field analytics and insights, market access and patient service analytics, or other related strategic analytics and management consulting
• Expertise in synthesizing complex data from diverse sources to drive actionable, business-aligned insights and inform strategic decisions across the product lifecycle
• Ability to work effectively with cross-functional teams and drive strategic brand initiatives, including ability to influence without authority
• Excellent communication skills, capable of effectively collaborating with senior leadership and aligning diverse teams around strategic goals for IPST
• Proficiency in insight and hypothesis generation, behavioral science, data science, and primary and secondary research methodologies