Director R&D Data Systems

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Data Analytics & Computational Sciences

Data Engineering

People Leader

Raritan, New Jersey, United States of America, Spring House, Pennsylvania, United States of America, Titusville, New Jersey, United States of America

We are searching for the best talent for a Director, R&D Data Systems to be located in Titusville, NJ, Spring House, PA or Raritan, NJ.

The Director, Cross R&D Data Systems, Innovative Medicines is responsible for leading shared data technology capabilities that enable trusted, governed, discoverable, interoperable, and reusable data across the Innovative Medicine R&D ecosystem. The role ensures that data platforms, quality controls, cataloging, master data, ingestion, transformation, and cross-functional data products are operated as enterprise-grade capabilities that support analytics, AI, GenAI, regulatory, safety, discovery, development, and operational use cases.

This role partners across data product teams, analytics/model teams, functional data owners, and business stakeholders to run an integrated Data & AI operating model. The role translates data strategy, governance requirements, data product needs, and business priorities into scalable platforms, data services, standards, scorecards, and operating practices.

The role is accountable for data quality and scorecards, data governance and standards, data catalog, master data management, R&D data platforms, data ingestion and transformation services, data virtualization platforms, and cross-functional data products across Innovative Medicine R&D.

• Define and operate data quality frameworks, scorecards, dashboards, thresholds, remediation routines, and executive reporting across priority R&D data domains and products.
• Partner with DDSAI (R&D Data Science Team), data owners, product teams, and business functions to define fit-for-purpose data quality rules, ownership, permitted use, and quality acceptance criteria.
• Establish automated quality monitoring for completeness, accuracy, timeliness, uniqueness, consistency, lineage, and domain-specific quality expectations.
• Translate data quality scorecard insights into remediation plans, product backlog priorities, governance decisions, and measurable improvements.
• Create transparency into data readiness for analytics, AI/GenAI, operational reporting, regulatory, safety, and scientific use cases.

• Implement data governance standards, decision rights, access workflows, data contracts, metadata expectations, permitted-use controls, lifecycle practices, and policy adherence across Cross R&D data systems.
• Partner with DDSAI data governance leaders, privacy, legal, Cybersecurity, quality, architecture, and business data owners to ensure governance is embedded into platforms and delivery workflows.
• Enforce standards for data domains, naming conventions, lineage, quality thresholds, stewardship, data sharing, retention, and compliant use.
• Establish governance routines that connect intake, prioritization, roadmap planning, data product ownership, standards compliance, and value realization.
• Enable consistent governance for structured, unstructured, semantic, operational, scientific, clinical, regulatory, and external data assets.

• Lead data catalog capabilities that improve discoverability, business context, technical metadata, ownership, lineage, permitted use, and reuse of R&D data assets.
• Integrate cataloging into data product delivery, ingestion workflows, transformation services, governance checkpoints, and operational support processes.
• Partner with DDSAI and data product owners to capture business purpose, data contracts, quality thresholds, semantic definitions, permitted use, and consumption patterns.
• Ensure catalog metadata connects source systems, transformations, data products, APIs, reports, AI/GenAI use cases, and downstream consumption.
• Drive adoption of catalog and lineage practices through enablement, automation, standard workflows, and transparent metrics.

• Lead technology capabilities supporting master…