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Tier-1 pharma brand, metro role, popular product title, and broad AI/pharma requirements increase candidate competition.
Requires pharma commercial analytics, pharma data expertise, and regulated AI governance, limiting cross-industry transferability.
Explicit 7+ years, pharma analytics mandate, and deep AI/product experience create strict screening.
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Co-own and drive the strategy, roadmap, and stakeholder alignment of the Agentic MMx and Always on Insights analytics platforms, focusing on advanced capabilities like scenario simulation and real-time decision support.
Integrate AI and decision-science outputs into commercial workflows (annual planning, CRM, omnichannel orchestration) to ensure adoption and business impact, including monitoring and governance of analytics tools and data health.
Lead operationalization and governance of autonomous AI agents and LLMs in commercial analytics, defining product KPIs, enablement content, and acting as a strategic thought partner to commercial stakeholders across multiple teams and timezones.
7+ years of experience in pharma commercial analytics or decision science with hands-on development and deployment of AI-driven analytical solutions.
Advanced degree preferred (MS/PhD) in Data Science, Statistics, Computer Science, Econometrics, or related quantitative field; BA/BS with strong relevant experience also accepted.
Experience managing and mentoring analytics teams within a global enterprise environment.
Hands-on expertise with agentic AI solutions (multi-agent systems, LLMs), embedding AI platforms into commercial workflows, and strong knowledge of pharmaceutical data sources.
Experienced in leading cross-functional global analytics teams and managing enterprise-scale AI product lifecycles in regulated environments such as pharmaceuticals.
Technically proficient in AI product management, with strong skills in AI governance, model monitoring, causal inference, and operationalizing complex analytical models in commercial settings.
Strategic thinker able to collaborate closely with diverse commercial and technical stakeholders, translate analytics into business value, and prioritize competing demands effectively.