Associate Director, Discovery Informatics

Johnson & Johnson Johnson & Johnson · Pharma · Spring House, PA +2

Associate Director, Discovery Informatics role at Johnson & Johnson, leading a team to build next-generation scientific technology for drug discovery, focusing on peptide and small molecule discovery. Key deliverables include genAI design pipelines, virtual screening workflows, and applications for molecule design. The role involves defining a technical roadmap, ensuring delivery of tools for non-experts, and cross-functional partnership. Requires advanced degree, 8+ years of experience in life science informatics, strong coding (Python), database expertise, and understanding of drug discovery workflows. Preferred qualifications include ML in molecular design and familiarity with agentic systems.

What you'd actually do

  1. Lead and mentor a high-performing team of scientists and engineer-scientists focused on developing scientific technologies for multiple modalities with a focus on peptide and small molecule discovery. Team deliverables include, but are not limited to, genAI design pipelines for peptide hit finding, ultra-large virtual screening workflows for small molecules, and end-user applications for molecule design and SAR analysis.
  2. Define and execute a technical roadmap that advances our in-house informatics capabilities and chemistry-focused data analytics applications
  3. Ensure the team delivers robust and roll out-ready tools and platforms to computational non-experts to enable self-service analysis
  4. Provide hands-on technical guidance and review
  5. Act as cross-functional partner to IT, chemistry, screening, and other wet-lab and in silico discovery groups to align deliverables with research needs, prioritize work, and translate scientific requirements into reliable tools

Skills

Required

  • Advanced degree (PhD preferred) in Computational Chemistry, Cheminformatics, Bioinformatics, Biophysics, Computer Science with strong domain experience, or related field.
  • 8+ years of relevant industry experience in cheminformatics or related scientific technology roles
  • Extensive hands-on experience in life science informatics working with large-scale data aggregation, manipulation, integration, mining, and analysis, including structured and unstructured data sources
  • Excellent coding ability (preferably python)
  • expertise in databases (e.g., PostGres, Snowflake) and ETL frameworks
  • Solid understanding of drug discovery workflows — chemistry, screening, assay technologies, and common experimental data issues and artifacts.
  • Demonstrated ability to ideate, implement, and deliver informatics solutions with measurable impact on portfolio projects
  • Proven track record of partnering cross-functionally with chemists, biologists, screening teams, other in silico scientists and IT to deliver impactful tools and analyses.
  • Excellent communication skills, ability to explain technical trade-offs to scientific and non-technical stakeholders.
  • Strong commitment to integrity, accountability, and talent development.
  • High creativity, curiosity and willingness to continuously learn and adapt in a constantly changing digital landscape

Nice to have

  • People leadership and mentoring experience are highly preferred.
  • First-hand experience in small molecule or peptide drug discovery; experience in more than one modality is a plus.
  • Experience with machine learning applications in molecular design (sequence models, generative models, property prediction).
  • Familiarity with agentic systems and their application to pharmaceutical research.
  • Prior involvement in building or scaling internal platforms used by chemists and bench scientists (dashboards, analysis apps, data lakes).
  • Hands-on experience with cheminformatics toolkits and libraries and molecular modeling software (e.g., Schroedinger, Rosetta, MOE)
  • Experience with ultra high-throughput virtual screening and large chemical spaces
  • Proficiency in front-end technologies and visualization techniques, preferably with previous experience in the development of visualization tools to enable drug discovery
  • Experience in software engineering, cloud architecture, and CI/CD

What the JD emphasized

  • genAI design pipelines for peptide hit finding
  • ultra-large virtual screening workflows for small molecules
  • end-user applications for molecule design and SAR analysis
  • machine learning applications in molecular design
  • agentic systems and their application to pharmaceutical research

Other signals

  • genAI design pipelines for peptide hit finding
  • ultra-large virtual screening workflows for small molecules
  • end-user applications for molecule design and SAR analysis
  • advances our in-house informatics capabilities
  • chemistry-focused data analytics applications
  • deliver robust and roll out-ready tools and platforms to computational non-experts
  • machine learning applications in molecular design
  • agentic systems and their application to pharmaceutical research