Senior Staff Scienitst-Quantitative Modeling, AI & Pharmacometrics

Job Function Summary:

Applies advanced computational, computer science, data science, statistical, and quantitative modeling principles, together with domain expertise in pharmacology, drug development, and translational science, to perform research and technology development supporting model-informed drug development (MIDD). Responsibilities include the design, development, implementation, validation, and application of computational models, machine learning approaches, simulation frameworks, and quantitative decision-support tools used to advance drug regimen development and clinical translation. The position integrates diverse preclinical, clinical, and real-world datasets to develop predictive models that support regimen optimization, dose selection, trial design, and translational decision-making. Research activities may include pharmacometric modeling, quantitative systems pharmacology (QSP), mechanistic and Bayesian modeling, artificial intelligence and machine learning methods, statistical analyses, and development of computational workflows and scientific software. This specialty exists for positions whose primary responsibility is to conduct independent quantitative research and use computational and data science technologies to advance biomedical and translational research.

Generic Scope

Technical leader with a high degree of knowledge in the overall field and recognized expertise in specific areas; problem-solving frequently requires analysis of unique issues / problems without precedent and / or structure. May manage programs that include formulating strategies and administering policies, processes, and resources; functions with a high degree of autonomy.

Custom Scope

The Savic Integrated Pharmacology Laboratory at UCSF seeks a senior quantitative scientist to lead the development and application of advanced computational, statistical, pharmacometric, and machine learning methodologies to support model-informed drug development (MIDD) within the PReDiCTR-TB Consortium. The incumbent will apply expertise in pharmacometrics, quantitative systems pharmacology, AI/ML, computational biology, and translational modeling to develop predictive frameworks that inform regimen optimization, dose selection, clinical trial design, and translational decision-making for infectious disease drug development. The position requires scientific leadership across multiple complex projects and collaboration with academic, industry, and regulatory stakeholders. The incumbent will independently design, develop, validate, and deploy quantitative models and computational tools that integrate preclinical, clinical, and real-world datasets, and will contribute to publications, grant applications, and strategic scientific initiatives across the consortium.