Principal Scientist, AI Co-Scientists - Synthetics Development

Job description

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: Beerse, Antwerp, Belgium Job Description: Principal Scientist, AI Co-Scientists - Synthetics Development Why this role matters: The future of small-molecule discovery and development depends on both translating scientific challenges into trustworthy AI-enabled capabilities and creating the data, information, and knowledge foundations on which those capabilities depend. This role will help Synthetics Development build and evaluate practical solutions spanning data science, machine learning, generative AI, information retrieval, document understanding, structured knowledge, and agentic workflows. Position Summary In this role, you will work at the interface of small-molecule science, data science, and artificial intelligence. In close partnership with chemists, analytical scientists, formulation scientists, process engineers, data scientists, and technology teams, you will investigate, prototype, and evaluate AI-enabled capabilities that address priority scientific challenges. The role combines two closely connected responsibilities. You will de-risk new ideas through hands-on experimentation, focused technical spikes, and limited-scope proofs of concept. You will also design and implement the document-processing, metadata, retrieval, structured-data, ontology, and knowledge-graph components required to make scientific information usable by these solutions. In both areas, you will translate scientific needs into testable technical approaches and make feasibility, limitations, dependencies, risks, and potential value visible before broader investment. Working as part of a cross-functional team, this role will contribute to scientific co-scientists, intelligent search, scientific data products, workflow automation, decision-support tools, and agentic workflows. The work will span practical experimentation and the implementation of reusable information foundations, including ingestion and document understanding, metadata and entity extraction, retrieval, controlled vocabularies, ontologies, and knowledge graphs. The role is not responsible for foundation-model development or production platform ownership. Its focus is hands-on development, technical learning, and creating robust components and evidence that help the team decide what to build and scale. Why This Role Is Unique This is a rare opportunity to help shape AI-native Synthetics Development by combining direct experimentation on real scientific challenges with the hands-on construction of the information and knowledge foundations required for credible scientific AI. You will work with scientists to understand their workflows, build and test targeted solutions, and ensure that the underlying scientific content can be processed, structured, retrieved, and reused effectively. The resulting prototypes, ingestion and retrieval pipelines, structured knowledge assets, reusable components, experimental findings, and technical recommendations will inform capabilities across small-molecule discovery and development, including chemistry, formulation, process engineering, analytical sciences, and increasingly lab-integrated workflows. Key Responsibilities Applied AI Prototyping & Technical De-risking • Design, implement, and evaluate limited-scope proofs of concept for priority scientific use cases. • Run focused technical spikes to test assumptions, compare approaches, and assess emerging AI, machine-learning, and scientific-computing technologies. • Translate scientific needs into testable hypotheses, representative experiments, success criteria, and clear technical questions. • Rapidly build prototypes that make feasibility, limitations, dependencies, risks, and potential value visible before broader investment. • Document experimental setup, results, trade-offs, and recommendations so that findings are reproducible and actionable. • Identify when an approach should be refined, handed over for engineering and scale-up, or discontinued. Scientific AI & Data Science • Apply data science, machine learning, generative AI, information retrieval, and agentic approaches to scientific challenges in small-molecule discovery and development. • Build experimental workflows that combine scientific data, documents, models, knowledge sources, APIs, and specialist tools. • Investigate the behavior of AI-enabled workflows, including the quality of outputs, evidence use, tool interactions, failure modes, and sensitivity to input or configuration choices. • Contribute reusable code, components, datasets, and experimental patterns that accelerate subsequent scientific AI work. • Use quantitative and qualitative evidence to communicate what works, what does not, and what is required to progress a concept. • Make scientific data AI-ready and science-ready by structuring, contextualizing, and curating experimental, analytical, process, molecular, and modeling data so it can be consumed by scientists, predictive models, co-scientists, and agents. • Assess the AI-readiness of candidate data sources, identifying gaps in quality, completeness, context, and accessibility, and prioritize data improvements that are driven by concrete scientific use cases rather than completeness for its own sake. Scientific Information, Retrieval & Knowledge Foundations • Work with subject-matter experts to understand how scientists find, interpret, combine, and use information in their work. • Design and test approaches for scientific search, retrieval, question answering, and information reuse. • Contribute to document processing, metadata, structured representations, controlled vocabularies, ontologies, or knowledge graphs, applying FAIR (findable, accessible, interoperable, reusable) principles pragmatically when these are needed to solve the scientific problem. • Evaluate combinations of semantic search, keyword search, filtering, aggregation, structured queries, and knowledge-based methods. • Help diagnose whether failures arise from data quality, missing knowledge, retrieval, reasoning, tool use, orchestration, or unclear requirements. • Build reusable, well-documented data pipelines and data products, including ingestion, transformation, and contextualization, that connect data across laboratory, process, and scientific systems and make it accessible through APIs for AI, analytics, and automated workflows. • Partner with data, architecture, and IT teams to leverage existing enterprise data platforms rather than recreating them locally, and provide the scientific perspective on semantics, provenance, and access. Cross-Functional Delivery & Continuous Learning • Act as a practical bridge between scientific SMEs and AI, data science, product, architecture, and engineering teams. • Support co-design sessions, pilots, user testing, and technical demonstrations with scientific communities. • Collaborate with engineering and platform teams to define the requirements and boundaries for solutions that are selected for further development. • Contribute