# Scientific Leader, Pre-clinical DMPK Drug Discovery Modeling

**Company**: ViiV Healthcare (GSK)
**Location**: Branford, CT
**Work arrangement**: hybrid
**Experience**: senior
**Job type**: full-time
**Salary**: $141,075 to $235,125
**Category**: Healthcare
**Industry**: Healthcare

**Apply**: https://gsk.wd5.myworkdayjobs.com/en-US/GSKCareers/job/USA---Connecticut---Branford/ViiV-Healthcare--GSK--Scientific-Leader--Pre-clinical-DMPK-Drug-Discovery-Modeling_447561-1?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_cd0ea721-29e

## Description

ViiV Healthcare, a global specialty HIV company, is seeking a Scientific Leader, Pre-clinical DMPK Drug Discovery Modeling. This role will drive model-informed drug discovery across ViiV's Drug Discovery organization by developing and applying fit-for-purpose modeling and simulation approaches.

The successful candidate will serve in a matrix leadership role, partnering closely with DMPK, Medicinal Chemistry, Biology, and Clinical Pharmacology to enable early dose predictions and guide the design and selection of high-quality clinical candidates.

Responsibilities:

- Develop and apply fit-for-purpose mechanistic and translational models (PBPK, IVIVE, PK/PD) to guide drug discovery teams toward the highest-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 that 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

Requirements:

- Master's degree with 7 or more years of post-graduate experience, or a PhD with 4 or more years of post-graduate experience, supporting drug discovery programs from lead optimization to Phase I clinical studies

- Strong understanding of ADME principles and integration of standard DMPK workflows and data streams into empirical and mechanistic modeling and simulation approaches

- Demonstrated ability to apply modeling and simulation approaches to influence key drug discovery decisions

- Broad computational programming expertise in programming and modeling languages (e.g., R, Python) and modeling platforms (e.g., Simcyp, GastroPlus)

- Experience integrating diverse datasets into unified predictive modeling platforms

- Ability to interpret and communicate modeling results to cross-functional teams

Preferred Qualifications:

- PhD with 6 or more 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

- Experience working across teams, including matrix and functional leadership

- Excellent written, verbal, and presentation skills

- Experience in infectious disease and/or immunology therapeutic areas

Benefits:

- Annual base salary ranging from $141,075 to $235,125

- Annual bonus and eligibility to participate in share-based long-term incentive program

- Health care and other insurance benefits

- Retirement benefits

- Paid holidays, vacation, and paid caregiver/parental and medical leave

## Skills

### Required
- ADME
- PBPK Modeling
- Pharmacokinetics
- Pharmacology
- Drug Discovery Process

### Nice to have
- infectious disease
- immunology
- matrix and functional leadership

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Source: [Apply at gsk.wd5.myworkdayjobs.com](https://gsk.wd5.myworkdayjobs.com/en-US/GSKCareers/job/USA---Connecticut---Branford/ViiV-Healthcare--GSK--Scientific-Leader--Pre-clinical-DMPK-Drug-Discovery-Modeling_447561-1?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)
