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Job Function:

Data Analytics & Computational Sciences

Job Sub Function:

Data Science

Job Category:

People Leader

All Job Posting Locations:

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

Job Description:

Johnson & Johnson Innovative Medicine is recruiting for a Director, R&D Data Science & Digital Health – OCMO (Office of the Chief Medical Officer).  This position has a primary location of Spring House, PA but is also open to Titusville, NJ.

Our expertise in Innovative Medicine is informed and inspired by patients, whose insights fuel our science-based advancements. Visionaries like you work on teams that save lives by developing the medicines of tomorrow.

Join us in developing treatments, finding cures, and pioneering the path from lab to life while championing patients every step of the way.

Learn more at https://www.jnj.com/innovative-medicine

Role Overview

We are seeking a dynamic leader to help drive the data science and data engineering strategy for our OCMO (Office of the Chief Medical Officer) organization.

The OCMO Data Science team applies advanced analytics and data engineering to forecast potential patient safety signals using our scientific understanding of disease biology across patient cohorts, and to interpret incoming adverse event (AE) signals in partnership with safety organizations. The team also uses patient safety signals we receive (AE's) to partner with our PSTS organization to better understand their possible cause, and feed those insights back to our Discovery organization for the design of our next generation of molecules.  This role will lead a team to build end-to-end capabilities that connect biology-informed hypotheses to scaled analytics—spanning AI-ready data engineering, predictive modeling, and insight-to-Discovery translation. 

A central expectation of this role is to ensure the organization has robust, future-proof data pipelines, governed data products, and traceable data foundations that enable safety signal forecasting and rapid signal interpretation at scale.

Key Responsibilities

• Lead development and deployment of analytics & AI approaches to support safety signal detection and translational interpretation.
• Apply model governance, versioning, and validation practices aligned with R&D AI expectations.
• Build analytic workflows to ingest, triage, and interpret AE/safety signals with safety partners, generating actionable hypotheses.
• Design, develop, and maintain scalable data pipelines to acquire, integrate, and manage relevant R&D/safety data from diverse sources.
• Transform raw inputs into standardized, analysis-ready, AI-ready datasets for modeling and decision support.
• Build and evolve data repositories and data models; optimize data flows for structured and unstructured data.
• Implement data quality and performance standards, KPIs, and monitoring to ensure accuracy and consistency.
• Establish data versioning and lineage to support traceability, compliance, and documentation of data architectures/workflows.
• Partner with data engineering and ontology/knowledge-graph teams to advance harmonized scientific data models and interoperability.
• Translate safety insights into clear feedback for Discovery to inform next-generation molecule design (closed-loop learning).
• Define priorities and align stakeholders on a roadmap; communicate progress and recommendations to senior leadership.
• Build, mentor, and lead a high-perform