Associate Director, QSP Modeling - Military Veterans

Site Name: USA - Pennsylvania - Upper Providence, UK - Hertfordshire - Stevenage
Posted Date: Oct 17 2024

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 mechanistic mathematical models and disease platforms 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 in order 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.

This role will provide YOU the opportunity to lead key activities to progress YOUR career, these responsibilities include some of the following.

• 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 QSP opportunities in combining QSP with other analytical approaches and build synergies through collaboration with computational groups including human genetics and functional genomics
• Perform scientific rigor and biological suitability assessment of QSP models and methodologies through establishing a context-driven verification & validation process (reviewing QSP model goals, assumptions, methodology, model code, model outputs, uncertainty quantification)

Why You?

Basic Qualifications:
We are looking for professionals with these required skills to achieve our goals :

• PhD in Applied Mathematics, Engineering, Pharmaceutical Sciences, Systems Biology, or related disciplines with experience in the application of mathematical and statistical methods
• 2+ 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++)
• Experience developing mechanistically-sound PK-PD models
• Experience translating, condensing, summarizing outcomes of modeling and simulation analyses into information that can be understood by project teams.

Preferred Qualifications:
If you have the following characteristics, it would be a plus:

• Solid biological understanding of immunology and previous experience in immuno-oncology modeling
• Experience working with common DMPK modeling tools (e.g., SimCYP, R)
• Demonstrated collaborative ways of working and able 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

#LI-GSK

Please visit GSK US Benefits Summary t o learn more about the comprehensive benefits program GSK offers US employees.

Why GSK?

Uniting science, technology and talent to get ahead of disease together.

GSK is a global biopharma company with a special purpose - to unite science, technology and talent to get ahead of disease together - so we can positively impact the health of billions of people and deliver stronger, more sustainable shareholder returns - as an organisation where people can thrive. We prevent and treat disease with vaccines, specialty and general medicines. We focus on the science of the immune system and the use of new platform and data technologies, investing in four core therapeutic areas (infectious diseases, HIV, respiratory/ immunology and oncology).

Our success absolutely depends on our people. While getting ahead of disease together is about our ambition for patients and shareholders, it's also about making GSK a place where people can thrive. We want GSK to be a place where people feel inspired, encouraged and challenged to be the best they can be. A place where they can be themselves - feeling welcome, valued, and included. Where they can keep growing and look after their wellbeing. So, if you share our ambition, join us at this exciting moment in our journey to get Ahead Together.

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