Research Data Manager

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. Responsibilities Build the Data Engine: Develop, optimize, and manage scalable relational databases, data systems, and automated pipelines that support multi-center research activities. 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. Architect DMS Solutions: Design and execute comprehensive data management and sharing plans covering storage, secure access control, data integrity, and disaster recovery. 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. Drive Data Harmonization: Collaborate with internal data scientists and external global partners to integrate and harmonize highly fragmented preclinical and clinical trial datasets. 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. Train and Enable Researchers: Develop training materials and provide ongoing instruction to consortium investigators, staff, and trainees on data management procedures, quality standards, data governance requirements, and best practices. Foster a culture of compliance, reproducibility, and data stewardship throughout the consortium. Generate Scientific Reports: Produce and review data listings, summaries, visualizations, and analytical reports for inclusion in scientific presentations, consortium deliverables, regulatory documents, manuscripts, and final study reports. Ensure all documentation is complete, accurate, reproducible, and audit-ready. Fuel Advanced Analytics: Actively support data visualization, analytics, and modeling efforts, structuring data mesh layers so they can be seamlessly consumed by machine learning and statistical pipelines. Qualifications Required Qualifications Bachelor's degree in related area and / or equivalent experience / training. Minimum 3 years of hands-on experience in database design, data pipeline engineering, and data harmonization or related experience. Technical Stack: Strong programming and querying skills across languages like SQL and Python or R (familiarity with tools like Stata, SAS or NONMEM data structures is a major plus). Environment: Direct experience working within research data environments, ideally supporting large-scale, NIH/state-funded programs. Communication: Exceptional communication skills with the ability to collaborate effectively across interdisciplinary teams of software engineers, pharmacometricians, and clinical investigators. Preferred Qualifications Master’s degree in Data Science, Computer Science, Bioinformatics, Health Informatics, or a closely related quantitative field. Prior experience navigating the data complexities of academic medical centers, consortia, or collaborative international research settings. Familiarity with clinical data ontologies and common data models (e.g., OMOP, CDISC, LOINC, or FHIR transfer protocols). #J-18808-Ljbffr