Associate Director, QSP Modelling

Associate Director, Quantitative Systems Pharmacology

Upper Providence, PA or Cambridge, MA

At GSK, we have bold ambitions for patients, aiming to positively impact the health of 2.5 billion people by the end of the decade. Our R&D focuses on discovering and delivering vaccines and medicines, combining our understanding of the immune system with cutting-edge technology to transform people's lives. GSK fosters a culture ambitious for patients, accountable for impact, and committed to doing the right thing, making sure that we focus our efforts on accelerating significant assets that meet patients' needs and have the highest probability of success.

We're uniting science, technology, and talent to get ahead of disease together.

Our approach to R&D

We are seeking an Associate Director level, Quantitative Systems Pharmacology Disease Lead for our Drug Metabolism and Pharmacokinetics (DMPK) department. Quantitative Systems Pharmacology (QSP) is a discipline that uses quantitative and mechanistic mathematical disease models to enhance the robustness and quality of decision-making from early research through clinical development. DMPK is a science-driven organization that drives the translational understanding of drug disposition and quantitative pharmacology to deliver transformational medicines with our partners.

• Build and utilize QSP models of biological, physiological, and pathophysiological processes to evaluate a disease, its pathways and progression, as well as drug candidates and treatment modalities
• Work in close collaboration with biologists, clinicians, clinical pharmacologists, QSP and nonclinical modelers, and other partner line colleagues to inform research programs and improve our understanding of disease mechanisms
• Serve as a disease-area QSP lead, possessing or developing expertise in disease pathophysiology, biomarkers, clinical endpoints, as well as in key experimental methodologies and data
• Serve as QSP modeling & simulation point-of-contact on project teams to solve challenging problems in drug research; contribute to preclinical study design and mechanistic interpretation of data
• Develop and/or utilize state-of-the-art mathematical tools to gain insight into causal relationships between individual components of system-level and drug-level responses of drug-target-biomarker-disease-patient interaction
• Analyze and interpret complex data sets in the context of disease mechanisms and pathways
• Explore new opportunities to combine QSP with other analytical approaches and build synergies through collaboration with computational groups working in areas related to human genetics, functional genomics, and AI/ML
• Perform scientific rigor and biological suitability assessment of QSP models and methodologies through applying a context-driven verification & validation process (reviewing QSP model goals, assumptions, methodology, model code, model outputs, uncertainty quantification)

• PhD in Applied Mathematics, Engineering, Pharmaceutical Sciences, Systems Biology, or related disciplines with experience in the application of mathematical and statistical methods
• 2 or more years of experience in Pharma Life Science Biomedical industry developing and applying QSP approaches to drug discovery or development programs
• Experience working with the theory, principles, statistical aspects of mathematical modeling and simulation, including numerical methods, parameter estimation/optimization, ordinary differential equations (ODEs), and how these can be applied in the development of complex models of biological pathways and systems
• Computational experience with one or more modeling and simulation packages or programming languages (e.g., MATLAB, Sim Biology, Julia, Python, C/C++)

• Solid biological understanding of immunology and previous experience in applying mechanistic modeling to metabolic, inflammatory, or fibrotic diseases
• Experience summarizing, condensing, and translating outcomes of modeling and simulation analyses into knowledge and insights that can be clearly understood by project teams
• Experience working with Large Language Models (LLMs) and Agentic AI (e.g., tool use and multi-agent orchestration)
• Demonstrated collaborative ways of working and ability to work successfully in matrix teams
• Ability to effectively communicate and interact with key stakeholders, medical professionals and senior leadership
• Ability to learn new…