Principal Scientist - Molecular Informatics & Computational Platforms

Johnson & Johnson Johnson & Johnson · Pharma · San Diego, CA +1

Johnson & Johnson is seeking a Principal Scientist to lead the transformation of drug discovery through agentic AI and computational platforms for In Silico Discovery. The role involves designing and implementing platforms for agentic workflows, defining best practices, and upskilling scientists. Responsibilities include exposing capabilities as agent-callable services, building domain agents, leading the design of scientific platforms for drug discovery stages, advancing small molecule informatics, and training scientists on AI tools.

What you'd actually do

  1. Serve as a key agentic-AI enablement leader for ISD: define best practices and help shape the department's agentic strategy.
  2. Expose computational science capabilities as agent-callable services and help build specialized domain agents, in partnership with platform engineering and portfolio teams.
  3. Lead design and development of scientific platforms and workflows—including virtual screening, active-learning triage, and ADMET-ML scoring—as reusable, agent-ready components for hit identification and compound optimization.
  4. Advance small molecule informatics across the DMTA cycle, guiding bench scientists in analysis and decision-making.
  5. Upskill ISD scientists in agentic-AI tools, prompt engineering, and AI-augmented research practices.

Skills

Required

  • PhD in Cheminformatics, Computational Chemistry, Chemical Engineering, or a related field.
  • Minimum of 3 years of industry experience
  • Hands-on experience with LLM-based tools for scientific workflows
  • skill/prompt engineering
  • MCP or RAG/knowledge-base integration
  • Deep expertise in cheminformatics software (e.g., KNIME, OpenEye, RDKit)
  • programming languages (e.g., Python, R)
  • scientific computing and ML libraries (e.g., pandas, scikit-learn, PyTorch)
  • Experience with SQL/Snowflake
  • modeling chemistry data (structures, assay and screening data)
  • cloud-native and containerized deployment environments
  • Experience working in cross-functional teams
  • translating ideas from bench scientists to informatics solutions
  • excellent problem-solving and communication skills

Nice to have

  • Advanced Analytics
  • Coaching
  • Critical Thinking
  • Data Analysis
  • Data Privacy Standards
  • Data Quality
  • Data Reporting
  • Data Savvy
  • Data Science
  • Data Visualization
  • Digital Fluency
  • Econometric Models
  • Organizing
  • Process Improvements
  • Strategic Thinking
  • Technical Credibility
  • Workflow Analysis

What the JD emphasized

  • proven track record of delivering end-to-end solutions from conceptualization to implementation
  • Hands-on experience with LLM-based tools for scientific workflows
  • skill/prompt engineering
  • MCP or RAG/knowledge-base integration

Other signals

  • agentic AI capabilities
  • computational platforms
  • LLM-based tools for scientific workflows
  • agent-callable services