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# Principal Scientist, Data Science - AI & Generative AI Systems - R&D DDSAI - Therapeutics Development & Supply (TDS)

- **Company:** Johnson & Johnson
- **Location:** Spring House, PA, United States
- **Experience:** Experienced
- **Salary:** $117,000.0 - $201,250.0
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Artificial Intelligence, Decision Support Systems, Information Retrieval, Python (Programming Language), Modular Design, Open Source Technology, Retrieval-Augmented Generation, Large Language Models, Model Validation, Generative AI, Agentic-AI, Information Technology, GXP
- **Published:** October 17, 2026
- **Apply:** https://www.jobmonkeyjobs.com/career/28116200/Principal-Scientist-Data-Science-Ai-Generative-Ai-Systems-R-D-Ddsai-Therapeutics-Development-Supply-Tds-Pennsylvania-Spring-House-1306

## About the Role

* Degree in Engineering, Data Science, Life Sciences, Computer Science, or related field; advanced degree preferred.
 * 3-5+ years of hands-on experience building and deploying AI, NLP, or generative AI solutions, including large language models (LLMs).
 * Experience in Pharmaceutical Product Development, including Chemistry, Manufacturing and Controls (CMC)
 * Strong proficiency in Python
 * Strong communication skills, with the ability to explain technical topics to non-technical stakeholders.

Preferred

 * Experience with open-source agentic frameworks (e.g., LangGraph, DSPy, mem0, etc.)
 * Experience with retrieval-augmented generation, embeddings, vector databases, and model evaluation.
 * Exposure to working in regulated environments (GxP)
 * Familiarity with responsible AI frameworks, content safety practices, and AI governance in regulated environments.

## Description

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., 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 infrastructure, architecture, evaluation, and reusable components.
 * Communicate system behavior, risks, and tradeoffs to both technical and non-technical stakeholders.