Director leading DSCS-wide analytics and business intelligence strategy in pharmaceutical company. Accelerating data-driven decision-making and overseeing digital technology initiatives.
Responsibilities
Lead the strategy, development, and governance of DSCS-wide analytics, data products, and business intelligence capabilities.
Accelerate data-driven decision-making across functions by providing high-value insights, modern BI solutions, and scalable digital platforms.
Define the vision for agentic business intelligence, lead a team of engineers/ analysts, standardize analytics workflows, and partner cross-functionally.
Develop and own the Agentic Business Intelligence Strategy aligned with enterprise digital transformation goals.
Ensure capabilities scale with business growth and scientific/technical needs.
Design and Implement an organization and DSCS wide GenAI / Agentic AI Framework strategy.
Build, mentor, and lead a high-performing team; create a culture of continuous improvement, innovation, and enable solutions that enable data-driven decision making.
Translate complex business needs into data-driven solutions; ensure BI solutions support strategic decision-making and operational excellence.
Develop and Implement strategy for GxP Usage of AI Solutions, partnering with Global Development Quality and stakeholders in Digital Manufacturing Division.
Requirements
Ph.D. with +6 years or B.S. with +8 years required with a concentration in a scientific-related discipline strongly preferred.
10+ years of experience across Business Intelligence, Analytics, Data Strategy, or Digital Transformation roles.
Understanding of GenAI / Agentic AI, and how to build orchestration across multiple agents
Experience in pharmaceutical research or manufacturing environments.
Proven leadership experience managing technical teams and enterprise stakeholders.
Expertise in BI platforms (Power BI, Tableau, Qlik), data modeling, SQL/ETL concepts, and cloud-based data environments.
Demonstrated ability to deliver insights in complex scientific, manufacturing, or technical environments.
Familiarity with small/large molecule manufacturing, development organizations, or digital enablement programs.
Exposure to AI/ML, digital twins, or advanced analytics frameworks.
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