Senior Director, Discovery Applied AI/ML
GSK
full-time
Posted on:
Location Type: Hybrid
Location: Upper Providence • Pennsylvania • 🇺🇸 United States
Visit company websiteJob Level
Senior
About the role
- Define and lead the applied AI/ML strategy for Discovery, aligned with the broader Data, Automation, and Predictive Sciences (DAPS) and Discovery Data Sciences roadmaps.
- Act as the primary applied AI/ML partner to discovery and RTech line leaders, embedding your team into portfolio projects across therapeutic areas and modalities.
- Drive a culture of pioneering applied AI/ML research, with emphasis on generative models for molecular and protein design.
- Partner with Discovery Data Sciences, Discovery Engineering, & Integration, Automation, Cheminformatics, Protein Design & Informatics, and R&D Digital & Tech to architect and deliver AI-augmented platforms for design, analysis, and decision support.
- Serve as a trusted thought partner to Discovery leadership on AI/ML.
Requirements
- Ph.D. in Computer Science, Machine Learning, Computational Biology, Computational Chemistry, Bioinformatics, Biophysics, or related quantitative discipline.
- 12+ years of experience in the pharmaceutical, biotech, technology, or closely related industry, with at least 8 years in leadership roles managing multi-disciplinary AI/ML or computational science teams.
- Demonstrated track record of applying modern AI/ML methods (including deep learning and generative models) to complex biological, chemical, or healthcare problems
- Deploying AI/ML solutions into production environments and achieving tangible impact on scientific or business outcomes.
- Experience working with multiple data modalities (e.g., sequence, structure, images, omics, chemical structures, clinical/real-world data) and integrating them into AI/ML workflows.
Benefits
- US Benefits Summary to learn more about the comprehensive benefits program GSK offers US employees
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
AIMLdeep learninggenerative modelscomputational biologycomputational chemistrybioinformaticsbiophysicsdata integrationAI/ML workflows
Soft skills
leadershipcollaborationstrategic thinkingcommunicationproblem-solvingthought partnershipcultural influenceteam managementmentorshipinnovation