Director, R&d Data Science & Digital Health – Ocmo (office of the Chief Medical Officer)

Johnson & Johnson Johnson & Johnson · Pharma · Spring House, Pennsylvania, United States of America, Titusville, New Jersey, United States of America

Lead the data science and data engineering strategy for the OCMO organization, focusing on building end-to-end capabilities for safety signal detection and interpretation. This involves AI-ready data engineering, predictive modeling, and translating insights back to the discovery organization. The role emphasizes robust data pipelines, governed data products, and traceable data foundations.

What you'd actually do

  1. Lead development and deployment of analytics & AI approaches to support safety signal detection and translational interpretation.
  2. Design, develop, and maintain scalable data pipelines to acquire, integrate, and manage relevant R&D/safety data from diverse sources.
  3. Transform raw inputs into standardized, analysis-ready, AI-ready datasets for modeling and decision support.
  4. Build analytic workflows to ingest, triage, and interpret AE/safety signals with safety partners, generating actionable hypotheses.
  5. Build, mentor, and lead a high-performing Data Science team for this new and exciting area.

Skills

Required

  • Advanced degree (MS or PhD) in Data Science, Biostatistics, Computational Biology, Biomedical Engineering, or related field.
  • Significant experience leading end-to-end Data & AI solutions in biomedical/life sciences contexts.
  • Demonstrated experience designing and delivering scalable data pipelines, data models/repositories, and AI-ready data products.
  • Experience implementing data quality standards, lineage/versioning, and documentation that enable traceability and reproducibility.
  • Proficiency with modern data engineering and analytics tooling (Python/R/SQL, cloud services, workflow orchestration, version control).

Nice to have

  • stakeholder leadership
  • delivery in a matrixed environment
  • translate business needs into engineering requirements
  • partner with data engineering and ontology/knowledge-graph teams
  • define priorities and align stakeholders on a roadmap
  • communicate progress and recommendations to senior leadership

What the JD emphasized

  • AI-ready data engineering
  • governed data products
  • traceable data foundations
  • scalable data pipelines
  • data quality and performance standards
  • data versioning and lineage

Other signals

  • AI-ready data engineering
  • predictive modeling
  • safety signal detection
  • translational interpretation
  • data pipelines
  • governed data products
  • traceable data foundations