product manager in pharma commercial

EPAM's Data & AI Practice helps pharmaceutical and life sciences companies develop analytics and AI strategies, data platforms, and AI-driven transformation programs that deliver measurable commercial value.

• Manage and coordinate consulting engagements end-to-end, owning delivery output, quality, and client satisfaction;
• Drive major pillars of broader data strategy and transformation programs;
• Discover data, analytics, and AI opportunities with customer business stakeholders;
• Design and drive executive workshops and stakeholder interviews;
• Define and prioritize strategic and immediate business needs;
• Map customer vision and requirements to data products, AI products, platforms, and solutions;
• Drive collaborative ideation sessions with client stakeholders and internal cross-functional teams;
• Envision, prototype, and oversee analytical solutions based on commercial domain expertise and business goals;
• Drive guided analytics using non-standard visualization techniques and ad-hoc analytics in executive discussions;
• Define success measures with clients and track realized value after go-live;
• Track adoption levels and change management required to achieve adoption;
• Collaborate with data engineering and architecture teams;
• Participate in decisions regarding team structures, composition, and talent selection;
• Partner with account managers to build, grow, and retain strategic customer relationships;
• Participate in pre-sales and up-sales initiatives;
• Navigate high ambiguity before discovery phases to understand client business needs;
• Respond to RFxs, create proposals, and present them to potential clients;
• Develop sales and field force analytics, including territory alignment, HCP targeting, incentive compensation, and engagement effectiveness;
• Develop marketing and brand analytics, including market share, competitive benchmarking, market landscape, and launch performance analytics;
• Develop customer engagement and omnichannel solutions, including HCP/HCO 360 data products and next-best-action models;
• Develop market access, patient, and payer analytics;
• Build commercial data foundations, including syndicated data integration, CRM platforms, claims and real-world data harmonization, and master data management;
• Develop AI and conversational analytics, including natural-language query assistants, commercial copilots, insight synthesis agents, and agentic workflows.

• 10+ Years of total experience in data, analytics, and/or AI;
• 3+ Years in product, delivery, consulting, or presales roles;
• 5+ Years of experience in Life Sciences / Pharma;
• Expert knowledge of Pharma Commercial domains, including sales, marketing, customer engagement, brand strategy, and omnichannel analytics;
• Experience managing enterprise data products across the full product lifecycle, including data platforms, data warehouses, data lakes, BI, and advanced analytics solutions;
• Experience managing AI products and understanding how they differ from analytics products;
• Ability to facilitate and drive executive-level strategy discussions, including value articulation and value realization tracking;
• Experience working with end-users, driving product design, running UATs, and facilitating and measuring adoption;
• Experience with end-to-end delivery of cloud data platforms, BI/reporting, data catalogs or marketplaces, analytics migration and modernization, predictive analytics and ML, generative AI, and agentic systems;
• Exposure to complex multi-market or cross-domain products and programs;
• Track record of leading project teams of 10+ individuals;
• Excellent communication and presentation skills, with comfort in technical and executive discussions;
• Ability to manage customer expectations and explain project deliverables to senior stakeholders;
• Ability to have honest conversations about AI capabilities and limitations;
• Ability to frame a presales opportunity into a customer engagement;
• Passion for the Data & AI space and a track record of staying ahead of the curve;
• Growth mindset and willingness to learn, experiment, and expand professional comfort zones;
• Nice to have: Familiarity with industry data and platforms such as IQVIA, Veeva, and SAP; German, Spanish, or French language proficiency.

• No conditions specified