Senior Staff Scientist-Quantitative Modeling, AI & Pharmacometrics
 University of California - San Francisco | |||||||||||||||||||||||||||||||||||||||||||
$121,900 - $199,500
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 United States, California, San Francisco | |||||||||||||||||||||||||||||||||||||||||||
| 1700 4th Street (Show on map) | |||||||||||||||||||||||||||||||||||||||||||
 Jul 28, 2026 | |||||||||||||||||||||||||||||||||||||||||||
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Job 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. About the Role The Savic Integrated Pharmacology Laboratory at UCSF is seeking a Ph.D. level Quantitative Scientist, Pharmacometrician, Computational Scientist, Data Scientist, or Translational Modeler to play a scientific leadership role within the PReDiCTR-TB Consortium, a global collaboration accelerating next-generation tuberculosis (TB) treatment regimens. This role sits at the forefront of model-informed drug development (MIDD), AI-enabled translational science, and quantitative decision-making for infectious diseases. The successful candidate will help shape quantitative strategies that directly influence TB regimen design, dose optimization, translational prediction, and development decisions across academia, industry, and regulatory stakeholders. We are particularly interested in intellectually curious, self-directed scientists who thrive at the intersection of computational science, biology, engineering, pharmacology, econometrics, and real-world decision-making. This is not a traditional support role. This is an opportunity to help define how AI, quantitative modeling, and translational science reshape infectious disease drug development globally. What You'll Work On You will contribute to high-impact translational and computational research programs that may include:
Key Responsibilities
Who We're Looking For We are seeking scientists who:
Preferred Scientific Backgrounds Candidates may come from one or more of the following fields:
Important Note About Qualifications We are not looking for candidates who possess every possible technical skill listed in this description. PReDiCTR-TB is intentionally designed as an interdisciplinary consortium where impactful innovation emerges from teams with complementary expertise. We highly value candidates with deep strength in one or several relevant domains who are excited to collaborate across disciplines and expand their quantitative toolkit. Candidates with strong expertise in the following areas are particularly encouraged to apply, even if they do not have formal training across all areas of pharmacometrics:
Department Overview: The Savic Integrated Pharmacology Laboratory in the Department of Bioengineering and Therapeutic Sciences at the University of California, San Francisco (UCSF) is a global leader in model-informed drug development (MIDD) for infectious diseases. The laboratory develops and applies quantitative approaches, including pharmacometrics, quantitative systems pharmacology (QSP), machine learning, translational pharmacology, and mechanistic modeling, to accelerate the development of optimized treatment regimens for tuberculosis (TB), HIV, malaria, and other diseases affecting global health. The laboratory leads and coordinates the Preclinical Design and Clinical Translation of Regimens for Tuberculosis (PReDiCTR-TB) Consortium, an international collaboration that integrates computational science, translational pharmacology, clinical data, and quantitative decision science to improve the efficiency and success of TB drug development. Through the use of predictive modeling, simulation, artificial intelligence, and advanced analytics, the consortium supports regimen selection, dose optimization, trial design, and translational decision-making across the drug development lifecycle. The Savic Lab maintains a highly collaborative and interdisciplinary research environment that brings together pharmacometricians, computational scientists, data scientists, engineers, clinicians, and biologists to address complex challenges in infectious disease drug development. The laboratory collaborates extensively with academic institutions, government agencies, nonprofit organizations, and pharmaceutical and biotechnology partners worldwide to translate scientific discoveries into improved patient outcomes. DUTIES & ESSENTIAL JOB FUNCTIONS Identify the functions or tasks that employees in the job perform. The essential functions should state the purpose of the work and the results to be accomplished, rather than how the function is performed. Of the tasks listed, what percentage of time is devoted to each? The more time employees spend on a function, the more likely it is that the function is essential. Generally, include those functions that account for 10% or more of the work, i.e., key items that contribute significantly to the achievement of the job. The functions should add up to 100%.
Required Qualifications
Preferred Qualifications
Required Qualifications
Preferred Qualifications
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