CMPTL & DATA SCI RSH SPEC 4 RP
About The Position
This role involves a hybrid of computational, data, and cyberinfrastructure (CI) dominated fields such as bioinformatics, geological information services (GIS), data analytics, and computational chemistry. The position applies computational, computer science, data science, and cyber infrastructure (CI) research and development principles, combined with relevant domain science knowledge, to conduct research and technology integration and development. Responsibilities include the research, design, development, analysis, operation, and support of high-performance computing (HPC) and data science research, software, tools, and hardware resources. The role also involves developing data algorithms and performing computations, statistical analyses, interpretation, and reporting of research. This specialty/function is for positions whose primary responsibility is research, utilizing computational and data science technology as a tool to achieve research objectives. The Preclinical Design and Clinical Translation of Regimens for Tuberculosis (PReDiCTR-TB) Consortium is a model-informed drug development (MIDD) platform that integrates computational science, translational pharmacology, and global clinical insight to accelerate the design and delivery of transformative TB regimens. PReDiCTR-TB functions as a strategic intelligence engine for regimen development, linking preclinical evidence, synthetic experiments, mechanistic models, and clinical data into a unified predictive framework. It embeds quantitative systems pharmacology (QSP), AI-driven analytics, and probabilistic decision modeling throughout development, enabling real-time prioritization of regimens with the highest probability of clinical success. The consortium takes a regimen-first, translation-driven approach, optimizing combinations, dosing strategies, and treatment durations through iterative simulation-validation cycles grounded in human-relevant biology, reducing reliance on empirical experimentation and increasing translational fidelity. The market for Model-informed drug development (MIDD) and AI in Clinical Trials is experiencing significant growth. Data and model-driven efficiencies in patient selection, drug repurposing, and real-time trial monitoring are compressing drug development timelines. At the UCSF Savic Lab, the focus is on driving this shift by establishing a modern, highly interconnected, and secure data & model infrastructure. The lab is seeking a solution-minded, highly technical, and mission-driven Distributed Data Pipeline Architect to design, build, lead implementation, and operate a research-grade, scale-distributed data architecture that will serve as the foundation for advanced analytics, multi-institution translational science, and accelerated drug development decision support. The Savic Lab is a global leader in model-informed drug development for infectious diseases and serves as a quantitative innovation hub for translational pharmacology, AI-enabled modeling, and next-generation regimen design. The laboratory conducts research across TB, HIV, malaria, pediatric infectious diseases, translational PK/PD, and systems pharmacology. Through its leadership role in PReDiCTR-TB, the lab collaborates with international partners to integrate computational science, mechanistic modeling, and clinical translation into actionable strategies that improve global health outcomes.
Requirements
- Computational, computer science, data science and cyber infrastructure (CI) research and development principles.
- Relevant domain science knowledge.
- Solution-minded, highly technical, and mission-driven.
Responsibilities
- Research, design, development, analysis, operation, and support of high performance computing (HPC) and data science research, software, tools and hardware resources.
- Develops data algorithms and performs computations, statistical analyses, interpretation and reporting of research.
- Design, build, lead implementation, and operate a research-grade, scale-distributed data architecture.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
