Strategic Lead, Quantitative Systems Pharmacology

GSK; Collegeville (PA); On-site; USD 188,000 - 314,000; Full time

Benefits offered by this job

Health care benefits

Annual bonus

Retirement benefits

Job summary

GSK in the United States is seeking a Director, Quantitative Systems Pharmacology to lead QSP/QST modeling across CPQM and the Drug Discovery portfolio. This role focuses on building mechanistic models to inform decision making from discovery through development.

You will collaborate with biologists, clinicians, pharmacometricians and data scientists to advance Model-Informed Drug Discovery and ensure robust, data-driven strategies for disease programs.

Qualifications

• PhD, MD, or PharmD with experience in mechanistic modelling and systems biology
• 7+ years in mechanistic modelling, inverse problems, and scientific ML in drug development
• Experience collaborating with senior stakeholders in cross-functional teams
• Proven implementation of Model-Informed Drug Discovery and Development (MIDD) approaches

Responsibilities

• Lead development and evaluation of mechanistic QSP/QST models for disease and drug action
• Coordinate strategy across multiple expertise areas for 3–24 month horizons
• Collaborate with biologists, clinicians, pharmacometricians, statisticians and imaging specialists
• Promote best practices in modelling and simulation to support R&D decisions
• Represent CPQM on internal advisory boards and leadership teams

Skills

Mechanistic modelling

Systems biology

Mathematical modeling

Inverse problem modelling

Scientific machine learning

Stakeholder management

MIDD

Education

PhD in a relevant field

MD or PharmD

Job description

GSK in the United States is seeking a Director, Quantitative Systems Pharmacology to lead QSP/QST modeling across CPQM and the Drug Discovery portfolio. This role focuses on building mechanistic models to inform decision making from discovery through development.

You will collaborate with biologists, clinicians, pharmacometricians and data scientists to advance Model-Informed Drug Discovery and ensure robust, data-driven strategies for disease programs.