Director, Data Science - AI Evidence Extraction

Position Summary

The Director will lead the design, development, and deployment of AI-enabled capabilities (e.g., agentic workflows) for scientific evidence extraction, curation, and quantitative synthesis in support of clinical development for Neuroscience R&D. Working closely with clinical, statistical, regulatory, scientific, and data teams, the candidate will translate priority evidence needs into scalable tools and reusable data assets that improve how information from the scientific literature and other sources is identified, structured, evaluated, and analyzed. This hands-on technical leader will set technical direction, guide multidisciplinary delivery, and build and validate robust evidence workflows. The role will establish fit-for-purpose data models, provenance and quality standards, evaluation and human-review best practices, and scientifically rigorous analytical approaches, while ensuring alignment with enterprise platforms, responsible AI expectations, and data-use requirements. Success will depend on partnering across functions to build workflows, drive their adoption, communicate uncertainty and tradeoffs, and deliver measurable value for clinical development decisions.

Why This Role Is Unique

Most AI systems produce answers that are quickly forgotten. This role will build a curated, provenance-bearing evidence asset that makes each subsequent analysis faster, less costly, and more reliable. The Director will choose evidence domains, ensure compliant data use, and establish standards that other programs can reuse. Success at twelve months means Neuroscience analysts begin meta-analyses by querying this evidence store, reviewers can trace pooled values to their cited source without assistance, and additional therapeutic areas seek to adopt the capability. The work will accelerate Johnson & Johnson's clinical development, advance AI-enabled evidence synthesis, and ultimately benefit patients.

Key Responsibilities

• Set the strategy, roadmap, priorities, and measures of value for scientific evidence data capabilities, focused on Neuroscience.
• Build and deploy scalable AI-enabled workflows (e.g., AI agents) for evidence identification, extraction, curation, and analysis.
• Own delivery, investment decisions, and outcomes for AI-based scientific evidence extraction.
• Work with the 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 (e.g., 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.

Qualifications

• PhD in a relevant quantitative, clinical, or life-science field; or MS with additional relevant experience.
• 8+ years of experience in pharmaceutical, biotech, or healthcare analytics, with 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.
• Exposure to regulated environments, multimodal document extraction, AI governance, and responsible AI is preferred.