Data and Partnerships Associate
A 501(c)(3) science-and-technology research institute/foundation operating direct Neuro-AI research, life-sciences programs through Radial, and an open-science residency program.
About Astera Institute
Astera Institute supports and operates open public-goods work for science and technology. Its current activities include neuroscience-informed AGI research, AI-enabled life-sciences programs, open datasets and publishing infrastructure, and residencies for early-stage projects.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will oversee research data throughout its lifecycle, uphold data quality and FAIR standards, and develop scalable data infrastructure. You will guide partners and researchers on data management, maintain federal partnerships, support knowledge-graph integration, and address responsible data-sharing needs.
Requirements
- Manage data in research organizations, academic institutions, or similar environments with complex data governance
- Apply data quality principles, data governance frameworks, and metadata standards
- Understand scientific data formats, repositories, and standards
- Build partnerships with outside organizations, particularly federal-government partners
- Translate technical data requirements for researchers
- Manage multiple data projects
- Understand FAIR data standards
- Understand open science
Responsibilities
- Serve as a contact for the National Science Foundation Programmable Cloud Lab Test Bed Network partnership
- Consult partners on preparing and publishing high-quality reusable data
- Ensure partners follow data policies
- Initiate and maintain federal-government partnerships
- Maintain documentation, metadata tools, standards, and data-release practices
- Train partners and researchers on data-management practices
- Identify and address technical gaps in data infrastructure
- Integrate research data with open-science knowledge graphs
- Develop responsible data-release approaches for sensitive, private, safety-related, or dual-use research