Principal Scientist, Scientific Workflows & Agentic Cheminformatics

Johnson & Johnson

Confirmed live yesterday High trust
Hybrid

Quick summary

Work type
Hybrid
Location
Spring House, PACambridge, MATurnhout, Antwerp, BelgiumBeerse, Belgium
Salary
$117,000–$201,250 / yr
Employment
Full-time
Posted
15 days ago
Freshness
Confirmed live yesterday

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $226k
This role $159k
$98k $294k
below market most similar roles pay here above market

This role pays less than 87% of similar roles. Most pay $186,993–$264,137 — the blue band above. At the midpoint, this role pays about $159k versus about $226k for comparable roles.

Based on 240 similar postings.

Employer

About Johnson & Johnson

Johnson & Johnson is a multinational corporation operating in three main segments: consumer health products, pharmaceuticals, and medical devices, known for brands like Tylenol, Band-Aid, and Janssen. Industry: Pharmaceuticals & Medical Devices

Johnson & Johnson currently has 45 open roles on FindRole.

Listed pay typically runs $109,000–$201,250 across 37 roles with salary data.

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At a glance

TL;DR · Principal Scientist, Scientific Workflows & Agentic Cheminformatics

The Principal Scientist, Scientific Workflows & Agentic Cheminformatics joins the In Silico Drug Discovery team to lead the scientific design, prototyping, and validation of agent-enabled workflows. This role focuses on identifying how agentic approaches can transform cheminformatics and discovery practices by integrating data, models, and expert knowledge to improve discovery speed and quality. The successful candidate will build trusted partnerships with medicinal chemists and biologists to translate complex scientific questions into reusable workflows. Key responsibilities include developing agentic prototypes in realistic discovery settings and evaluating outputs for scientific validity and reproducibility. Required expertise includes a PhD or equivalent experience in cheminformatics or computational chemistry, strong Python programming, workflow orchestration, and experience with large language models. The work addresses high-value discovery questions by creating automated pipelines and human-agent collaboration patterns.

What you'll do

  • Lead the scientific design, prototyping, and validation of agent-enabled workflows for drug discovery.
  • Identify opportunities to use agentic approaches to integrate data, models, and expert knowledge.
  • Translate complex scientific questions into reusable workflows that make assumptions and expert judgments explicit.
  • Develop and test agentic prototypes in realistic discovery settings using predictive models and laboratory methods.
  • Evaluate agent behavior for scientific validity, reproducibility, uncertainty, and relevance to discovery decisions.
  • Partner with data science and platform engineering teams to implement AI architecture and production security.
  • Drive adoption of agentic capabilities by integrating them into familiar scientific environments and communities of practice.
  • Provide technical leadership to set priorities, success measures, and guide trade-off decisions for agentic prototypes.

What we're looking for

  • PhD or equivalent substantial experience in cheminformatics, computational chemistry, medicinal chemistry, chemical biology, or a related discipline.
  • Recognized depth of scientific expertise relevant to drug discovery, including compound design and predictive models.
  • Strong Python programming skills with experience developing automated workflows, pipelines, or scientific applications.
  • Familiarity with workflow orchestration and software development practices including version control, testing, and CI/CD.
  • Hands-on experience using AI-assisted development to build scientific software and applying large language models.
  • Demonstrated ability to define scientific direction in ambiguous areas and influence stakeholders across scientific and technology organizations.
  • Track record of scientific leadership, cross-disciplinary collaboration, and mentoring scientists and engineers.
  • Ability to travel domestically and internationally up to 10% of the time.
  • Experience designing and evaluating end-to-end agent-enabled scientific workflows or multi-agent solutions (preferred).
  • Strong knowledge of and hands-on experience with public and commercial bioactivity and chemogenomics databases (preferred).
  • Demonstrated track record of partnering with platform engineering teams to develop production-ready solutions (preferred).

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