Principal Scientist, Data Science

At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity.

Learn more at jnj.com

As guided by Our Credo, Johnson & Johnson is responsible to our employees who work with us throughout the world. We provide an inclusive work environment where each person is considered as an individual. At Johnson & Johnson, we respect the diversity and dignity of our employees and recognize their merit.

Job Function

Data Analytics & Computational Sciences

Job Sub Function

Data Science

Job Category

Scientific/Technology

All Job Posting Locations

Malvern, Pennsylvania, United States of America, Spring House, Pennsylvania, United States of America

Job Description

Johnson & Johnson Innovative Medicine is recruiting for Principal Scientist, Data Science - AI & Generative AI Systems - R&D DDSAI - Therapeutics Development & Supply (TDS).

The primary location for this position is open to Spring House, PA; Malvern, PA;

J&J Innovative Medicine develops treatments that improve the health of people worldwide. Research and development areas encompass oncology, immunology, neuroscience, cardiopulmonary and specialty ophthalmology. Our goal is to help people live longer, healthier lives. We have produced and marketed many first-in-class prescription medications and are poised to serve the broad needs of the healthcare market - from patients to practitioners and from clinics to hospitals. To learn more about Johnson & Johnson Innovative Medicine visit https://innovativemedicine.jnj.com/

POSITION SUMMARY

The R&D Data Science organization is seeking a Principal Scientist, Data Science to design, build, evaluate, and deploy AI and generative AI systems that accelerate scientific, regulatory, and technical workflows across Therapeutics Development & Supply (TDS).

This hands-on individual contributor works across Product Development, Manufacturing, Clinical Supply Chain, Quality, and Digital and Data Science teams to deliver LLM applications, agentic workflows, and AI-driven automation solutions specific to TDS. Key use cases include scientific and regulatory document authoring, evidence-based response generation, knowledge retrieval and synthesis, technical decision support, and workflow automation. The role is aligned to TDS priorities and collaborates closely with DDSAI AI/ML on shared architectures, reusable components, evaluation standards, responsible AI practices, and scalable deployment. A successful candidate has a strong background in pharmaceutical CMC development and can develop AI solutions in collaboration with AI/ML teams.

KEY RESPONSIBILITIES

AI Solution Development & Delivery

• Design, build, test, and deploy AI solutions for documentation, knowledge retrieval, evidence synthesis, and decision support in the TDS ecosystem.
• Build agentic AI workflows that coordinate models, tools, data, and human review across multi-step scientific and supply chain processes.
• Develop reusable AI capabilities aligned with DDSAI AI/ML architecture and platform standards.
• Translate scientific, regulatory, and operational needs into scalable AI systems for regulated environments.

Technical Rigor & Responsible AI

• Establish evaluation frameworks for LLM systems, including rubrics, traceability, robustness, uncertainty, and usability.
• Apply responsible AI practices, including human-in-the-loop validation, risk assessment, content safety, and governance.
• Maintain strong software engineering practices, including modular design, testing, documentation, and lifecycle management.
• Contribute to best practices for LLM, RAG, and agentic AI systems across DDSAI and TDS.

Cross-Functional Collaboration

• Partner with regulatory, quality, CMC, scientific, and technical stakeholders to define use cases, validate outputs, and drive adoption.
• Collaborate with DDSAI AI/ML on