Principal Scientist, Portfolio & gRED Interface-Biologics, AI for Drug Discovery (AIDD)
$201,300–$373,800 year
On-siteSouth San Francisco, California, United States
Job Summary
Lead the application of machine learning models for developability, functional modeling, and design optimization to active gRED antibody projects, translating research into measurable portfolio outcomes. Serve as the primary technical advisor to gRED leadership on computational strategy and act as the liaison between the Computational Sciences CoE and discovery organizations. Build and mentor a team of computational scientists, setting research direction and aligning technical approaches with pRED counterparts. Navigate complex stakeholder landscapes across Antibody Engineering, platform teams, and external partners to drive collaboration and prioritize initiatives. Own high-impact research initiatives that de-risk projects and accelerate timelines in large molecule discovery.
Required Qualifications
- PhD in Computational Biology, Biophysics, Immunology, Chemistry, or Computer Science
- 10+ years total experience
- 5+ years in ML/computational methods for biologics
- Expertise in computational methods for large molecule design
- Depth in developability assessment
- Depth in biophysical modeling
- Depth in antibody engineering
- Track record of developing novel computational methods that have influenced real drug discovery projects
- First-author publications demonstrating research innovation in computational drug discovery or related fields
- Understanding of the full lifecycle of antibody discovery: target selection, lead optimization, humanization, and development
- Proven ability to translate computational research into portfolio impact
- Experience working on active drug discovery projects where work directly influenced decisions, design choices, or project prioritization
- Understanding of the pressures and constraints of portfolio science: timelines, resource constraints, competing priorities, the need for both speed and rigor
- Ability to communicate uncertainty and limitations to non-expert audiences
- Experience explaining what models can and cannot do
- Experience working across scientific teams and navigating organizational complexity
- Demonstrated track record leading research teams or mentoring junior scientists
- Ability to attract, develop, and retain talented people
- Strong communication skills: can present complex science to senior leadership and collaborate across disciplines
- Strategic Thinking: You see where AI/ML can have outsized impact in drug discovery and why some approaches work better than others
- Understanding of the organizational dynamics of large pharma R&D and how to navigate them effectively
- Comfortable with ambiguity but disciplined about execution
- Have opinions about how things should work, and you drive toward better approaches
- Relocation benefits are NOT available for this job posting
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