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Research Data Manager

Job in San Francisco, San Francisco County, California, 94199, USA
Listing for: University of California - San Francisco
Full Time position
Listed on 2026-07-13
Job specializations:
  • Research/Development
    Data Scientist
Salary/Wage Range or Industry Benchmark: 120000 - 150000 USD Yearly USD 120000.00 150000.00 YEAR
Job Description & How to Apply Below

Job Summary

We are in the midst of a massive, data-driven transformation in medicine. Driven by the push for streamlined drug development, the market for advanced analytics and AI in clinical research is expanding exponentially.

The PReDiCTR‑TB Consortium is not just following industry standards‑we are creating and leading them. Our work focuses on radical TB drug development data integration, utilizing cutting‑edge computational and AI‑driven approaches to advance drug development and precision dosing for infectious diseases and vulnerable special populations.

We are moving past the static, isolated spreadsheets of the past. To power the next generation of machine learning models and Drug‑Informed Drug Development, we need a solution‑minded, highly organized Research Data Manager/Database Administrator. You will be the architect of our data liquidity, designing and maintaining the data systems that turn complex, raw data into a structured, scalable asset for global research collaborators.

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 and serves as an innovation hub for translational pharmacology, quantitative systems pharmacology (QSP), pharmacometrics, machine learning, artificial intelligence, and mechanistic modeling. The laboratory develops and applies cutting‑edge computational and quantitative approaches to accelerate the discovery and optimization of treatment regimens for tuberculosis (TB), HIV, malaria, pediatric infectious diseases, and other conditions impacting global health.

As the coordinating center for the international Preclinical Design and Clinical Translation of Regimens for Tuberculosis (PReDiCTR‑TB) Consortium, the laboratory integrates computational science, predictive modeling, translational pharmacology, clinical data, and quantitative decision science to support regimen selection, dose optimization, clinical trial design, and model‑informed decision‑making across the drug development lifecycle. The Savic Lab fosters a highly collaborative, interdisciplinary, and collegial research environment where pharmacometricians, computational scientists, data scientists, engineers, clinicians, and biologists work together with academic, government, nonprofit, and industry partners worldwide to solve complex translational challenges and translate scientific discoveries into improved patient outcomes.

Duties

& Essential Job Functions
  • Build the Data Engine:
    Develop, optimize, and manage scalable relational databases, data systems, and automated pipelines that support multi‑center research activities.
    30% of time
  • Own the Data Lifecycle:
    Design, implement, and maintain the Savic Lab's data collection processes, ensuring that research data are accurately captured, validated, transformed, and stored. Manage the complete data lifecycle from initial raw data acquisition across multiple internal and external research partners through harmonization, analysis‑ready dataset creation, long‑term archival, and secure storage.
    20% of time
  • Architect DMS Solutions:
    Design and execute comprehensive data management and sharing plans covering storage, secure access control, data integrity, and disaster recovery.
    20% of time
  • Ensure Research Compliance:
    Ensure that all data management practices comply with NIH, institutional, consortium, and regulatory requirements. Maintain awareness of evolving regulations, standards, and best practices related to research data governance, security, sharing, and reproducibility. 5% of time
  • Drive Data Harmonization:
    Collaborate with internal data scientists and external global partners to integrate and harmonize highly fragmented preclinical and clinical trial datasets. 5% of time
  • Establish Technical Standards:
    Create standard operating procedures (SOPs) and data‑quality frameworks that align directly with NIH Data Management and Sharing (DMS) policies and FAIR principles. 5% of time
  • Train and Enable Researchers:
    Develop training materials and provide ongoing…
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