Principal Scientist, AI Co-Scientists

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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.

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 progres