Associate Director, QSP Modelling

GSK•Upper Providence, PA

1d•$157,575 - $262,625•Hybrid

About The Position

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. We are seeking a highly motivated individual to develop and apply QSP models within discovery to aid in the prioritization, selection, understanding, and optimization of promising treatments for a variety of diseases and therapeutic areas. The successful candidate will be responsible for integrating data and knowledge in a highly collaborative and multi-disciplinary environment to develop a deeper understanding of physiological systems and disease mechanisms with the goal of influencing target prioritization, modality evaluation, and delivering molecules with transformational potential into the clinic.

Requirements

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, SimBiology, Julia, Python, C/C++)

Nice To Haves

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 areas of biological sciences and build on solid foundation of quantitative skills
Experience with the application of scientific machine learning (SciML) approaches to mechanistic modeling
Experience integrating omics and/or other multi-modal data with mechanistic models

Responsibilities

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)