Director - Quantitative Systems Pharmacology

GSK, Plc.Collegeville, PA
70d

About The Position

The Department of Clinical Pharmacology and Quantitative Medicine (CPQM) in Respiratory, Immunology and Inflammation Research Unit, R&D GSK is recruiting for a Director-level Quantitative Systems Pharmacologist Lead to join the Quantitative Systems Pharmacology team. The CPQM at GSK is a newly established organization with the remit to become a Centre of Excellence in Model-Informed Drug Development (MIDD). It uniquely integrates clinical pharmacology, digital medicine, translational imaging, and mechanistic & systems modeling. The convergence of science and technology is changing discovery and development at GSK, allowing us to make advances on behalf of patients that were once thought impossible. Our R&D organization is delivering more innovation, better and faster, using new data and platform technologies that speed discovery and development and improve the chance of success. This position represents a key opportunity for professionals with PhD, MD, PharmD or equivalent doctoral background, who are experienced mechanistic modelers with experience in QSP and/or QST modelling, clinical pharmacology and pharmacometrics to advance the vision and mission of GSK's rapidly expanding Respiratory, Inflammation and Immunology Disease portfolio. Sought after experiences for this position include building mechanistic mathematical models and leveraging the knowledge in the areas of scientific ML, inverse problems, AIML and/or statistical methodologies, to enhance the robustness and quality of model development to leverage for decision-making from exploratory research through clinical development. You will be expected to play a critical role in the day-to-day driving integration of end-to-end model-informed drug discovery and development through building and applying QSP and QST models. In addition, you will be defining, implementing and coordinating QSP and QST related Clinical Pharmacology and Quantitative Medicine development strategies for disease and therapeutics of interest, and providing expert input into the clinical pharmacology evidence generation and integrated evidence plans in the Disease therapeutic area. This is an exciting opportunity to bring your vision and leadership to a new era of digital innovation in clinical pharmacology and quantitative medicine, profoundly impacting patient outcomes and shaping the future of R&D at GSK.

Requirements

  • PhD, MD, or PharmD with experience in mechanistic modelling and simulation and systems biology with applications in pharmaceutical research and development
  • Substantial experience in mechanistic mathematical modeling, inverse problem modeling and simulation, and/or scientific machine learning methodologies to apply to pre-clinical and/or clinical questions in drug development to solve practical problems in pharmaceutical industry
  • Strong drive and agility to quickly learn and build knowledge on a drug-disease system, the mechanism, endpoints, progression, prevention, treatments, and trial design
  • Demonstrated aptitude for productive collaboration in a multi-discipline team, using effective communication and taking personal accountability for timely delivery of results
  • Clear evidence of ability to make sound judgement in complex situations and adapt to changing business needs by prioritizing multiple tasks
  • Experience working with senior stakeholders in a cross functional environment
  • Track record of implementation of Model-Informed Drug Discovery and Development (MIDD) approaches to accelerate patient access to novel therapies and to expand therapeutic indications of marketed drugs

Nice To Haves

  • Prior experience in Respiratory, Hepatology and/or Infections diseases is a plus

Responsibilities

  • Build and/or guide mathematical model development to understand disease, its progression, and drug action to prevent, treat and cure diseases; conduct simulations to assess trial design performance
  • Apply mechanistic models of biological, physiological, and pathophysiological processes to evaluate a disease, its pathways and progression, as well as drug candidates or treatment modalities
  • Develop and/or utilize state-of-the-art mathematical tools including knowledge of inverse-problem modelling and simulation, scientific ML and/or statistical techniques to gain insight into causal relationships between individual components of system-level and drug-level responses of drug-target-biomarker-disease-patient interaction
  • Define and execute a coordinated scientific and technical strategy (18-24 months planning horizon) with demonstrated ability to co-ordinate outputs from several expertise areas to determine strategy
  • Work in close collaboration with biologists, clinicians, clinical pharmacologists, pharmacometricians, statisticians, AIML, imaging, biomarker and other partner line colleagues to inform research and development programs and improve our understanding of disease mechanisms
  • Implement best practices, trends, lessons learned from internal and external sources to further clinical pharmacology modelling and simulation contributions to R&D pipeline
  • Create a collaboration framework with internal and external experts in the development and application of these models
  • Learn and apply emerging modeling and simulation methodologies with a view to enhance clinical program efficiency and investment decision quality; collaborate with external field- leading teams for methodology application
  • Able to efficiently and effectively interact with line and middle management, staff and external contacts on a functional, strategic and tactical level
  • Represent QSP CPQM on various internal advisory boards, companywide initiatives and/or leadership teams
  • Promote transparency and communicate R&D achievements through publications in appropriate scientific journals

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What This Job Offers

Job Type

Full-time

Career Level

Director

Industry

Chemical Manufacturing

Education Level

Ph.D. or professional degree

Number of Employees

5,001-10,000 employees

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