Job Description

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.

Responsibilities

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)

30
Build the Data Engine: Develop, optimize, and manage scalable relational databases, data systems, and automated pipelines that support multi-center research activities.
20
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
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.
5
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.
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.
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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%.)

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).