Senior Scientist, Data Science in Madrid

€56,000.00 - €88,780.00

In addition to base pay, we offer the following benefits*: an annual bonus with set target (% of pay) depending on pay grade / location, where the actual amount is based on the employees' and companies' performance of the previous calendar year, or sales commissions. Moreover, we offer vacation days, parental leave for a minimum of 12 weeks, bereavement leave, caregiver leave, volunteer leave, well-being reimbursement, programs for financial, physical and mental health. We also offer service anniversary and recognition awards, and subject to the terms of their respective plans, employees - and in some location's eligible dependents - can participate in several insurance plans. For more information, visit Employee benefits | Supporting well-being & career growth | Johnson & Johnson Careers.

*This is for informative purposes only. Amounts and actual benefits may vary by location and are subject to change.

Job Function: Data Analytics & Computational Sciences

Job Sub Function: Data Science

Job Category: Scientific/Technology

The R&D Data Science organization is seeking a Senior Scientist, Data Science to contribute to the design, development, evaluation, and deployment of 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 develop 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. Working with guidance from senior technical colleagues in DDSAI, the role delivers defined components and workflows within agreed priorities and milestones, using established architectures, reusable components, evaluation standards, and responsible AI practices. A successful candidate combines practical AI development skills with an understanding of pharmaceutical CMC development and seeks technical input on complex design decisions, risks, and delivery challenges.

This is a rare opportunity to grow in one of the world's most ambitious and fast-growing R&D Data Science organizations, shaping how Therapeutics Development & Supply data powers next‑generation therapies in the largest biomedical company on the planet. Your work will directly accelerate Johnson & Johnson's scientific discovery, fuel AI innovation, and impact patients globally.

• Develop, test, and support deployment of AI solutions for documentation, knowledge retrieval, evidence synthesis, and decision support in the TDS ecosystem, with guidance on solution design and implementation.
• Build and test defined components of agentic AI workflows that coordinate models, tools, data, and human review across multi-step scientific and supply chain processes.
• Implement and adapt reusable AI capabilities using established DDSAI AI/ML architectures and platform standards.
• Work with scientific, regulatory, and operational stakeholders and senior technical colleagues to translate defined needs into AI solution requirements and implementations for regulated environments.
• Apply established evaluation frameworks for LLM systems, including rubrics, traceability, robustness, uncertainty, and usability; contribute test cases and analysis to improve these frameworks.
• Follow responsible AI practices, including human-in-the-loop validation, risk assessment, content safety, and governance, with guidance on identifying and escalating risks.
• Apply software engineering practices, including modular design, testing, documentation, and lifecycle management, with code review and technical guidance.
• Contribute implementation experience and evaluation findings to best practices for LLM, RAG, and agentic AI systems across DDSAI and TDS.
• Work with regulatory, quality, CMC, scientific, and technical stakeholders to clarify use-case requirements, validate outputs, and support adoption.
• Collaborate with DDSAI AI/ML to use shared infrastructure, established architectures, evaluation methods, and reusable components.
• Communicate implementation progress, evaluation results, system limitations, and risks to technical and non-technical stakeholders; seek guidance when issues affect scope, design, or delivery.