Biospecimens and Genetics Data Manager

Job Function Summary

Involves gathering, analyzing, and curating a wide variety of research data, primarily involving human biofluids and genetics but also other clinical data elements. Participates in research by supporting data QC, ingesting data from external collaborators, creating datasets to share with researchers, summarizing collected data, developing data best practices, and facilitating data related activities (e.g., biospecimens’ inventory, billing). Prepares reports, charts, tables, and other visual aids to interpret and communicate data.

The position is with the UCSF Edward and Pearl Fein Memory and Aging Center (MAC) ((Use the "Apply for this Job" box below).), a dynamic multidisciplinary clinical research program investigating neurodegenerative diseases. Under the supervision of the MAC Genetics and Biospecimen Manager and the Director of Research Technology, the Biospecimen and Genetics Data Manager will support and manage key aspects of program data workflow, data capture, data integration, results management, and reporting for more than 30 related specimen collection protocols involving multiple partners and laboratories.

Responsibilities include discrepancy management, manual data review, data cleaning, data transfers, data merging, generating metrics, and reporting. The role will also involve writing and maintaining programmatic scripts, providing hands‑on data management support, clarifying data validation errors, improving overall data quality, and supporting specimen transfers and data preparations for the Memory and Aging Center. The manager will report to the Genetics and Biospecimen Program Manager for domain‑specific work, and to the Director of Research Technology for data‑related work on an interim basis.

• Maintain and develop research gathering, retrieval, and reporting systems.
• Provide data extraction and reporting for specimen billing, including scripting and maintaining Excel data sheets.
• Provide data feasibility input (e.g., cohort counts, availability) to support study design by supporting biospecimen requests through data querying and subject identification.
• Prepare queries and reports for internal department constituents, and perform data querying and summary for data requests and routine reporting activities from internal and external researchers and clinicians.
• Assist in maintaining systems for organizing data to analyze, identify, and report trends, implementing and modifying scripts and queries, and functioning as a data power user in key systems supporting biospecimen data workflows.
• Aggregate and QC research specimen data; prepare and summarize information, and recommend actions. Regularly (daily/weekly) request, receive, and integrate new inventory and biospecimen result datasets (CSV/Excel) into central data repositories, checking records for integrity and resolving discrepancies with the specimens team and laboratory staff.

• Bachelor’s degree in a related area and a minimum of one year of experience or equivalent training.
• Working knowledge of research functions and systems programming, scripting, and statistical software packages.
• Strong analytical and consulting skills.
• Excellent communication skills, able to convey complex information clearly and concisely in both verbal and written forms.
• Proven track record of managing and maintaining accurate data in a complex data environment.
• Proficiency in statistical analysis, data transformation, data systems programming, or data schema design and documentation.
• Knowledge of Freezerworks LIMS or similar systems.
• Ability to multitask, be organized, and work quickly in a dynamic environment with competing priorities.
• Prior experience in a research environment, preferably in a wet‑lab or closely with a wet‑lab.
• Excellent Excel and MS Office skills.
• Proficiency in a programming language (Python, R, etc.).
• Experience with source control management (e.g., Git) and best practices for scripting.

• Minimum of three years of research or clinical data management experience.
• Knowledge of HIPAA and best practices for protecting health information.
• Experience working with relational databases or software tools to design queries and reports.
• Basic understanding of genetics and proteomics.