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About the job

JOB SUMMARY

Involves gathering, analyzing, and interpreting a wide variety of research data. Designs and conducts research including selecting data samples, developing research instruments, analyzing collected information according to established statistical methods, and developing recommendations based on research findings. Prepares reports, charts, tables, and other visual aids to interpret and communicate data and results.

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.

What You’ll Do

This is a frontline infrastructure role. You will bridge the gap between complex biomedical research and modern data engineering, ensuring our data is robust, clean, secure, and AI-ready.

Build the Data Engine: Develop, optimize, and manage scalable relational databases, data systems, and automated pipelines that support multi-center research activities.

Architect DMS Solutions: Design and execute comprehensive data management and sharing plans covering storage, secure access control, data integrity, and disaster recovery.

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 .

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.

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.

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.

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.

What You Need to Be Successful

We are looking for a self-driven puzzle-solver who loves building robust pipelines and thrives at the intersection of data architecture and translational science.

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

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%.

of time

Essential Function (Yes/No )

Key Responsibilities

(To be completed by Supervisor)

3 0

Develops systems for organizing data to analyze, identify and report trends.

Build the Data Engine: Develop, optimize, and manage scalable relational databases, data systems, and automated pipelines that support multi-center research activities.

2 0

Manages database of research data for projects.

2 0

Participates in development and implementation of data security policies and procedures. Keeps abreast of technical advances in storage, documentation and dissemination of computerized data.

Architect DMS Solutions: Design and execute comprehensive data management and sharing plans covering storage, secure access control, data integrity, and disaster recovery.

5 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 Drive Data Harmonization: Collaborate with internal data scientists and external global partners to integrate and harmonize highly fragmented preclinical and clinical trial datasets.

5 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.

May supervise data entry, database management and research analysis of students, support staff and/or lower level analysts.

5 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.

5 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.

100% (To update total %, enter the amount of time in whole numbers (without the % symbol - e.g., 15, 20) then highlight the total sum (e.g., 1%) at the bottom of the column and press F9. The total sum should add up to 100%.)

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).