Principal Scientist, Scientific Workflows & Agentic Cheminformatics Full-time

Principal Scientist, Scientific Workflows & Agentic Cheminformatics

Johnson & Johnson Innovative Medicine is recruiting for a Principal Scientist, Scientific Workflows & Agentic Cheminformatics to join our In Silico Drug Discovery team. This position will be located in either Spring House, PA; or Cambridge, MA and may require some travel as needed (no remote option). Consideration may be given for candidates located in Beerse, Belgium.

The Principal Scientist is a scientific leader who identifies how agentic approaches can transform cheminformatics and discovery practice, rapidly prototypes and validates new scientific workflows, and enables their adoption by scientists through familiar discovery environments.

Mission

The Principal Scientist will lead the identification, scientific design, prototyping, and validation of agent-enabled workflows that address high-value discovery questions across multiple use cases or discovery areas. This individual will also establish the scientific evidence, adoption model, and partnerships needed to advance promising concepts into broadly used capabilities.

Core responsibilities

• Build trusted partnerships with medicinal chemists, CADD scientists, biologists, DMPK scientists, data scientists, and scientific leaders to understand decision workflows and identify opportunities with impact across projects or discovery areas.
• Shape and prioritize a portfolio of scientific questions and decision points where agentic approaches can bring together data, models, and expert knowledge to improve the quality or speed of discovery.
• Define robust and reusable approaches for translating complex scientific questions into workflows that make evidence, assumptions, decisions, and points requiring expert judgment explicit.
• Design scientifically meaningful use cases, reference datasets, and success criteria that enable rigorous evaluation.
• Evaluate agent behavior and outputs for scientific validity, reproducibility, uncertainty, appropriate tool use, robustness across scenarios, and relevance to discovery decisions.
• Capture the scientific insights gained from early applications and use them to define which approaches merit further development and broader adoption.
• Develop and test agentic prototypes in realistic discovery settings, combining scientific expertise with relevant data, predictive models, computational or laboratory methods, and tools that agents can select and coordinate to address a scientific objective.
• Provide scientific and technical leadership across a portfolio of agentic prototypes, set priorities and success measures, guide trade-off decisions, and ensure that the work addresses meaningful discovery needs and delivers measurable value.
• Drive broader adoption of agentic capabilities by integrating them into familiar scientific environments, establishing effective patterns for human–agent collaboration, and building communities of practice across discovery functions.
• Lead the scientific design, prototyping, and validation of agent-enabled discovery workflows, and partner with data science and platform engineering teams from an early stage on the underlying AI architecture, production implementation, security, and sustained operation. Translate scientific requirements and evidence into reusable capabilities that align with shared architectures and can be deployed across discovery programs.

Required skills

• PhD or equivalent substantial experience in cheminformatics, computational chemistry, medicinal chemistry, chemical biology, or a related discipline, with recognized depth of scientific expertise relevant to drug discovery.
• Deep understanding of compound design, predictive models, and discovery decision-making, with the ability to connect scientific needs and opportunities across disciplines or programs.
• Strong Python programming skills and experience developing automated workflows, pipelines, or scientific applications to explore scientific questions and create practical proof-of-concept solutions.
• Familiarity with workflow orchestration and sound software development practices, including version control, testing, and continuous integration and delivery.
• Hands-on experience using AI-assisted development approaches to build reliable scientific software or automated workflows, with practical experience applying large language models and designing effective interactions between models, tools, and human experts.
• Demonstrated ability to define scientific direction in ambiguous areas, translate emerging needs into a coherent capability agenda, and influence stakeholders across scientific, data science, and technology organizations.
• Track record of scientific leadership, cross-disciplinary collaboration, and translating promising ideas into approaches that influence discovery strategy and outcomes beyond a single project.
• Ability to mentor scientists and engineers, raise scientific and technical standards, and build alignment and capability across a matrixed organization.
• Ability to travel domestically and internationally up to 10% of the time.

Preferred skills:

• Experience designing and evaluating end-to-end agent-enabled scientific workflows or multi-agent solutions is preferred.
• Strong knowledge of and hands-on experience with public and commercial bioactivity and chemogenomics databases
• Demonstrated track record of partnering with platform engineering teams to develop production-ready solutions