Open Science Data Steward

Responsibilities

• Partner with researchers across scientific disciplines to prepare and publish high-quality, reusable data for human researchers and AI systems.
• Oversee the full research-data lifecycle from creation through publication and archival, maintaining data integrity and accessibility.
• Develop living documentation and best practices for repository interactions, release timing and methods, metadata tools and standards, and FAIR data compliance.
• Identify cross-team data-management challenges and develop scalable solutions and infrastructure improvements.
• Integrate research data with open science knowledge graphs and emerging AI/ML ecosystems.
• Train and guide researchers and residents on data-management best practices and effective data sharing.
• Collaborate with software engineers to address technical gaps in data infrastructure.
• Ensure data policies are followed and resolve routine compliance issues and complex data-management edge cases.
• Develop responsible data-release approaches for privacy, safety, sensitivity, and dual-use concerns, escalating difficult cases when appropriate.

Requirements

• Bachelor's degree in Computer Science, Information Science, Library Science, Data Science, or a related field.
• 3–5+ years of experience managing data in research organizations, academic institutions, or similar environments with complex data-governance requirements.
• Deep understanding of data-quality principles, data-governance frameworks, and metadata standards.
• Familiarity with scientific data formats, repositories, and standards.
• Excellent communication and interpersonal skills, including the ability to translate technical data requirements for researchers.
• Strong organizational skills and meticulous attention to detail.
• Ability to manage multiple data projects while maintaining high standards.
• Ability to work independently, identify process improvements, and drive initiatives forward.

Nice-to-haves

• Experience working with data across multiple scientific disciplines, including life sciences, materials science, neuroscience, robotics, or atmospheric science.
• Familiarity with AI/ML data landscapes and structuring data for machine-learning applications.
• Knowledge of open science initiatives and open-data publishing platforms.
• Experience with data integration, knowledge graphs, or semantic web technologies.
• Experience developing data-management policies, procedures, or training materials.
• Advanced degree.