ED, Head of Analytics Engineering

Role Summary

This position will be located in the East Hanover, NJ site. The Insights and Decision Science (IDS) team is seeking a visionary and pragmatic leader to build and institutionalize analytics at scale. You will architect the systems, standards, and capabilities that enable high-quality, consistent, and scalable analytics across the organization, defining frameworks, ensuring rigor, and connecting cross-functional efforts to make analytics a repeatable, trusted enterprise capability.

Responsibilities

• Establish and champion analytics rigor, including statistical standards, validation protocols, and QA practices.
• Define enterprise-wide frameworks for measurement, performance metrics, and reporting standards.
• Enable cross-functional synergy by connecting analytics efforts across Commercial, Medical, Market Access, and other domains.
• Institutionalize analytics engineering as a core discipline, including reusability of data pipelines, analytics automation, and production-grade analytics solutions.
• Develop scalable capabilities that allow solutions to be transferred across use cases quickly and effectively.
• Support governance and compliance, ensuring analytical outputs meet regulatory and ethical standards.

Qualifications

• Required: 10+ years of experience in data/analytics, with demonstrated success in building scalable systems or frameworks.
• Required: Proven track record of establishing analytics standards, governance, or platform capabilities.
• Required: Strong cross-functional experience, ideally within Commercial, Medical, or Market Access analytics in life sciences or a regulated industry.
• Required: Experience with analytics engineering, BI tooling, and data infrastructure concepts.
• Required: Excellent communication and influence skills, especially with technical and non-technical stakeholders.
• Preferred: Systems thinker with a deep understanding of how analytics drive decisions across an enterprise.
• Preferred: Builder mindset: enjoys creating structure from ambiguity and scaling impact.
• Preferred: Comfortable balancing strategic design and operational execution.
• Preferred: Deep understanding of data lifecycle, from data ingestion to decision-making impact.

Education

• Bachelor's or master's degree in business administration, Computer Science, Engineering, or a related field.