Principal Scientist, Biologics Optimization

Johnson & Johnson Johnson & Johnson · Pharma · Spring House, PA +1

This role focuses on the design and optimization of biologic variants using AI/ML-enabled approaches to achieve differentiated functional outcomes for therapeutic modalities. The Principal Scientist will lead optimization efforts, apply computational and AI/ML methods to guide variant design and selection, and evaluate emerging technologies to accelerate discovery workflows.

What you'd actually do

  1. Lead biologics optimization efforts across multiple therapeutic programs, translating target biology and desired mechanisms of action into rational protein engineering strategies.
  2. Serve as a subject matter expert in function-driven optimization: develop and champion novel development efforts aimed at fulfilling strategic long-term antibody design/optimization initiatives and validation.
  3. Develop optimization strategies that integrate antibody sequence, structure, developability, and biological function to achieve program objectives.
  4. Lead/participate on project teams to advance discovery portfolio projects. Act as a point of accountability for optimization engineering asks and manage project timelines independently.
  5. Partner with teams across TD/TA to define critical functional hypotheses and establish screening cascades that enable MoA-driven decision making.

Skills

Required

  • MS Degree in Molecular Biology, Biochemistry, Biotechnology, Biomedical Engineering, structural biology or related field
  • 3 years of relevant industry experience in biologics discovery, protein engineering, or therapeutic optimization
  • Demonstrated scientific leadership and expertise in biologics optimization
  • Extensive experience in antibody and protein engineering
  • Deep understanding of antibody structure-function relationships, protein sequence space, and biological pathways

Nice to have

  • PhD is highly preferred

What the JD emphasized

  • AI/ML-enabled design approaches
  • computational and AI/ML-enabled design approaches
  • Apply computational, AI/ML, and data-driven approaches to guide variant design, library construction, sequence prioritization, and hit selection.

Other signals

  • AI/ML-enabled design approaches
  • computational and AI/ML-enabled design approaches
  • Apply computational, AI/ML, and data-driven approaches to guide variant design, library construction, sequence prioritization, and hit selection.
  • Evaluate emerging technologies and develop innovative approaches to accelerate biologics optimization workflows and therapeutic discovery.