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GSK

Develops medicines, vaccines, and consumer health

Scientific Leader, Pre-clinical DMPK Drug Discovery Modeling - Multiple Teams

Quantitative Biology & BiostatisticsConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • A Master’s degree with at least 7 years of post-graduate experience, or a PhD with at least 4 years of post-graduate experience, supporting drug discovery programs from lead optimization to Phase I clinical studies.
  • Strong understanding of ADME principles and the integration of standard DMPK workflows and data streams into empirical methods, including NCA and compartmental modeling, and mechanistic modeling and simulation, including PBPK and QSP, to support rapid discovery decision-making.
  • Demonstrated ability to apply modeling and simulation approaches to influence drug discovery decisions, including lead optimization, candidate selection, dose projection, and development strategy.
  • Broad computational programming expertise in programming and modeling languages such as R or Python, or equivalents, and modeling platforms such as Simcyp, GastroPlus, and Phoenix WinNonLin, to develop modeling and analysis workflows.
  • Experience integrating diverse datasets, including in vitro, in vivo, clinical, and, where applicable, omics data, into unified predictive modeling platforms.
  • Ability to interpret and communicate modeling results to cross-functional teams and through written communications such as internal reports and publications.

Responsibilities

  • Develop and apply fit-for-purpose mechanistic and translational models, including PBPK, IVIVE, and PK/PD, to guide drug discovery teams toward high-quality clinical candidates.
  • Translate complex ADME, PK, PD, and translational data into actionable insights for project teams and senior leadership.
  • Apply modeling expertise across diverse modalities, formulations, and routes of administration, including long-acting therapies.
  • Build scalable computational tools, workflows, and prototypes to accelerate discovery decisions and candidate optimization.
  • Establish best practices, standards, and governance for modeling methodologies, tools, and computational workflows.
  • Contribute to scientific publications, regulatory submissions, and other technical documentation, and represent ViiV externally through regulatory interactions and scientific thought leadership.

Desired Qualifications

  • PhD with at least 6 years of drug discovery experience in DMPK or a related discipline.
  • Demonstrated experience developing and applying modeling and simulation strategies across diverse modalities and routes of administration, including oral therapies, long-acting injectables, biologics, antibody-drug conjugates, lipid nanoparticles, and other advanced therapeutic platforms.
  • Experience working across teams, including matrix and functional leadership, and partnering with internal and external experts to deliver quality candidates with speed.
  • Strong written, verbal, and presentation skills, including experience communicating scientific concepts to cross-functional teams and key stakeholders.
  • Experience in infectious disease and/or immunology therapeutic areas.

About the Company

GSK

Develops medicines, vaccines, and consumer health

GSK is a global healthcare company focused on three main areas: Pharmaceuticals, Vaccines, and Consumer Healthcare. It develops medicines, vaccines, and consumer health products to improve health outcomes worldwide. Its products address diseases in respiratory, HIV, oncology, and immuno-inflammatory areas; vaccines for influenza, shingles, and COVID-19; and over-the-counter wellness products. The company relies on substantial R&D and strategic partnerships to bring new products to market and to address health needs. Revenues come from sales of medicines, vaccines, and consumer health items, often complemented by patient support programs and collaborations with governments and biotech partners.

Company size
10,001+
Company stage
IPO
Headquarters
London, United Kingdom
Founded
1891

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