Director, Data Science - AI Evidence Extraction

Johnson & Johnson Innovative Medicine•Hopewell Township, NJ

5d•$164,000 - $282,900

About The Position

Johnson & Johnson Innovative Medicine is recruiting a Director, Data Science – AI Evidence Extraction for its Research and Development Data, Data Science, and AI team. The role may be based at the company’s US sites in Raritan, New Jersey; Titusville, New Jersey; Cambridge, Massachusetts; or Spring House, Pennsylvania. It focuses on leading the design, development, and deployment of AI-enabled capabilities for scientific evidence extraction, curation, and quantitative synthesis in support of Neuroscience clinical development. The Director will translate evidence needs into scalable tools and reusable data assets, and will set technical direction while guiding multidisciplinary delivery. The role establishes fit-for-purpose data models, provenance and quality standards, evaluation and human-review practices, and scientifically rigorous analytical approaches. It also ensures alignment with enterprise platforms, responsible AI expectations, and data-use requirements. The position requires close partnership across clinical, statistical, regulatory, scientific, legal, data, and technology teams to drive adoption, communicate uncertainty and tradeoffs, and deliver measurable value for clinical development decisions. Travel may be required as needed.

Requirements

PhD in a relevant quantitative, clinical, or life-science field, or an MS with additional relevant experience.
8+ years of experience in pharmaceutical, biotech, or healthcare analytics, including experience setting technical direction.
Hands-on experience with systematic reviews, meta-analysis, or quantitative evidence synthesis.
Experience with data modeling, scientific data curation, provenance, and quality control.
Recent experience building and deploying scalable AI, NLP, or generative AI solutions.
Strong proficiency in Python and/or R, and sound software engineering practices.
Understanding of evaluation, validation, uncertainty, and human-in-the-loop review.
Ability to assess scientific evidence and make well-reasoned technical tradeoffs.
Demonstrated cross-functional leadership and influence without relying on managerial authority.
Strong communication skills with technical, scientific, and senior non-technical stakeholders.
Experience working within shared platforms, architecture standards, and governance frameworks.

Nice To Haves

Exposure to regulated environments.
Experience with multimodal document extraction.
Exposure to AI governance and responsible AI.

Responsibilities

Set strategy, roadmap, priorities, and measures of value for scientific evidence data capabilities, focused on Neuroscience.
Build and deploy scalable AI-enabled workflows for evidence identification, extraction, curation, and analysis.
Own delivery, investment decisions, and outcomes for AI-based scientific evidence extraction.
Work with data teams to establish fit-for-purpose data models and databases, provenance, quality controls, and reusable evidence assets.
Define validation, evaluation, human-review, and responsible AI practices.
Develop scientifically rigorous analysis workflows and quantify limitations such as uncertainty.
Partner with clinical, statistical, regulatory, scientific, legal, and technology stakeholders.
Shape shared platform capabilities and ensure alignment with architecture, governance, and data-use requirements.
Guide multidisciplinary delivery, maintain strong engineering practices, and mentor cross-functional contributors.
Communicate priorities, tradeoffs, risks, progress, and impact to senior stakeholders.