Senior Quantitative Scientist - TB Modeling & AI

University of California - San Francisco; San Francisco (CA); On-site; USD 120,000 - 150,000; Full time

Job summary

The University of California - San Francisco is hiring a Senior Quantitative Scientist to lead advanced computational studies aimed at model-informed drug development. This role emphasizes the application of statistical modeling, pharmacometrics, and machine learning methodologies, requiring in-depth collaboration with industry and regulatory partners.

The ideal candidate must have at least 5 years of relevant experience, excellent programming skills in Python or R, and the ability to drive scientific innovation in infectious disease drug development.

This position offers a dynamic research environment that integrates diverse datasets to influence clinical decision-making in drug development.

Qualifications

• 5+ years relevant experience in quantitative modeling.
• Advanced knowledge of pharmacometrics and statistical modeling.
• Proven track record in managing research projects.

Responsibilities

• Lead development of various modeling techniques.
• Integrate multi-source datasets and develop workflows.
• Mentor trainees and oversee project teams.

Skills

Pharmacometrics

Statistical modeling

Machine learning

Computational biology

Data integration

Education

Bachelor's degree in Computer / Computational / Data Science

Master's degree preferred

Tools

Python

R

Job description

The University of California - San Francisco is hiring a Senior Quantitative Scientist to lead advanced computational studies aimed at model-informed drug development. This role emphasizes the application of statistical modeling, pharmacometrics, and machine learning methodologies, requiring in-depth collaboration with industry and regulatory partners.

The ideal candidate must have at least 5 years of relevant experience, excellent programming skills in Python or R, and the ability to drive scientific innovation in infectious disease drug development.

This position offers a dynamic research environment that integrates diverse datasets to influence clinical decision-making in drug development.