VP, Applied AI, Data Science & Commercial Intelligence
New
N
NovartisPharmaceutical, Healthcare
Remote Position (USA)Full-TimeVp
SalaryUSD 303100 - 562900 / year
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Job Details
- Experience
- 15+ years
- Required Skills
- Artificial IntelligenceMachine LearningData engineeringData scienceMLOpsGenerative AI
Requirements
- 15+ years of progressive leadership experience across applied AI, data science, analytics, commercial intelligence, digital product, or technology transformation.
- Experience in pharmaceutical, healthcare, life sciences, biotech, or another regulated data-rich industry.
- Proven record setting AI/data strategy and scaling AI-enabled products and decision intelligence.
- Strong executive fluency across machine learning, GenAI, agentic AI, LLMs, RAG, data engineering, MLOps/LLMOps, and cloud platforms.
- Deep understanding of commercial business value drivers, including customer engagement, field effectiveness, omnichannel, launch excellence, and market access.
- Demonstrated ability to translate business strategy into portfolio priorities, operating models, and value realization metrics.
- Enterprise leadership capability to align commercial, technical, and legal functions around responsible AI adoption.
- Advanced degree in Computer Science, Data Science, Engineering, Statistics, or a related quantitative discipline.
Responsibilities
- Define and execute the enterprise strategy for applied AI, data science, and commercial intelligence aligned with business priorities.
- Lead multidisciplinary teams spanning AI, machine learning, data science, analytics, and commercial insights.
- Develop and deploy AI-powered products and decision-support solutions that improve customer engagement, commercial performance, and operational efficiency.
- Partner with business leaders to identify, prioritize, and deliver high-value AI and analytics use cases with measurable ROI.
- Establish best practices for AI governance, model lifecycle management, responsible AI, and data quality.
- Build scalable AI and data platforms that enable reusable models, standardized capabilities, and rapid innovation.
- Recruit, mentor, and develop world-class technical and product talent while fostering a culture of collaboration and continuous learning.
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