Pharmacometrician & Systems Pharmacologist

ROLE SUMMARY The Pharmacometrics & Systems Pharmacology (PSP) group is seeking a highly motivated quantitative scientist to support the Internal Medicine portfolio through the application of model-informed drug development (MIDD). This role will use pharmacometrics, systems pharmacology, and/or quantitative biology approaches to inform decision-making across drug discovery and clinical development.

The successful candidate will contribute to the development and application of quantitative models to support and accelerate the discovery and development of novel therapeutics. This individual will work in multidisciplinary teams to design studies, interpret data, and guide strategy using mechanistic and/or data-driven modeling approaches.

Candidates from diverse quantitative backgrounds, including pharmacometrics, systems pharmacology, applied mathematics, engineering, statistics, or related disciplines will be considered.

KEY RESPONSIBILITIES:

Quantitative Modeling & Simulation:

Develop and apply pharmacometric and/or mechanistic QSP models to support translational and clinical drug development

Perform quantitative analyses including (as appropriate):

Population PK/PD modeling

Exposure-response modeling (semi-mechanistic to empirical)
Disease progression modeling

Model-based meta-analysis

Systems pharmacology / mechanistic modeling

Integrate diverse data sources (preclinical, clinical, literature, biomarkers) for model development and validation

Integrate artificial intelligence into data workflows and analytics

Model-Informed Drug Development (MIDD)
Contribute to the development and execution of MIDD strategies across programs

Inform key decisions including:

Dose and regimen selection

Study design and optimization

Go/no-go decisions

Identify opportunities where modeling can enhance understanding of efficacy, safety, and disease biology

Cross-Functional Collaboration Partner closely with clinical pharmacology, statistics, clinical, translational, and biology teams

Serve as a quantitative expert on multidisciplinary project teams

Communicate modeling results and insights to technical and non-technical stakeholders

Scientific Leadership Contribute to internal scientific strategy and methodological innovation

Prepare high-quality technical reports, presentations, and regulatory documentation

Author or contribute to scientific publications and external presentations

Mentor junior scientists and contribute to best practices in quantitative pharmacology

BASIC QUALIFICATIONS:

0-3+ years post Ph.D. (or equivalent e.g. Pharm.

D., or M.D.) in:

Pharmacometrics, Pharmacokinetics/Pharmacodynamics, Systems Pharmacology Applied Mathematics, Engineering, Physics, Statistics Computational Biology, Mathematical Biology, or related quantitative discipline

Demonstrated experience in quantitative modeling and simulation applied to biological or clinical systems

Proven ability to work effectively in multidisciplinary teams

PREFERRED QUALIFICATIONS:

Postdoctoral research experience strongly preferred

Experience in metabolic diseases preferred (obesity, type 2 diabetes etc)
Experience applying MIDD approaches in pharmaceutical or biotechnology settings

Expertise in one or more of the following:

NONMEM, Monolix, or similar population modeling tools

R, Julia, MATLAB, Python, or equivalent computational platforms

Knowledge of drug development processes (preclinical to clinical)[VS3]Basic understanding and strong interest in biology and (patho)-physiology

Strong written and verbal communication skills

CANDIDATE PROFILEWe are seeking a scientifically curious, collaborative, and impact-driven individual who:

Brings strong quantitative rigor and problem-solving ability

Is comfortable working across modeling paradigms (mechanistic and statistical)
Can translate complex analyses into actionable insights

Thrives in a fast-paced, team-oriented research environment

IMPACT This role will directly contribute to advancing innovative therapies in Internal Medicine by enabling data-driven, model-informed decisions that improve the efficiency, robustness, and success of drug development programs.

Work Location Assignment:
Hybrid#LI-PFEThe annual base salary for this position ranges…