Position Introduction:

The University of Oklahoma Libraries seeks to recruit a Research Data and AI Specialist to advance research data services that support computational, reproducible, and AI-enabled research across the University Libraries system. As research increasingly relies on large, complex datasets and emerging computational methods, this position helps researchers manage, analyze, preserve, and responsibly use research data within computational and reproducible workflows. The Research Data and AI Specialist provides consultations, instruction, and embedded research support on research data management, computational and reproducible workflows, and the ethical application of artificial intelligence to research processes. Working collaboratively with Libraries and campus partners, the position scales services, cultivates data and AI literacy, and expands institutional capacity for responsible, transparent, reproducible, and sustainable research. Responsibilities include providing consultations and instruction; advising on data management plans, metadata standards, computational and reproducible research practices, and research data sharing; developing services that enable long-term stewardship and access to research outputs; supporting responsible AI-enabled research workflows; collaborating with campus partners to advance open science initiatives; and evaluating emerging technologies that strengthen research support across the university.

The successful candidate will be required to have the following skills and competencies: 

• Excellent communication skills, with the ability to explain technical concepts to non-technical audiences. 
• Demonstrated experience consulting with researchers or clients on technical topics. 
• Experience supporting computational, data-intensive, or research computing workflows. 
• Experience applying research data management principles, including documentation, metadata, data organization, preservation, or data sharing. 
• Experience using, evaluating, or supporting AI-enabled tools, computational methods, or machine learning applications in research, education, or professional settings. 
• Proficiency in scripting languages (Python and/or R) for data manipulation and analysis.
• Proficiency in Git/GitHub, research data repositories, and metadata management and documentation practices.
• Ability to listen to research needs and recommend appropriate solutions and develop scalable services and programs.
• Ability to design and deliver effective learning experiences for researchers with diverse technical backgrounds.
• Comfortable working across disciplines with varying data types and methodologies.
• Understanding research ethics, data privacy, and responsible AI use.
• Works effectively in team environments and builds cross-functional partnerships.
• Stays current with rapidly evolving AI and data technologies.
• Analytical thinking with the ability to troubleshoot complex technical challenges.
• Demonstrates intellectual curiosity and the ability to learn emerging technologies while translating them into practical research applications.

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Required Education and Experience:

• Bachelor's degree.
• 36 months of experience in computer programming and analysis.

Equivalency/Substitution: Will accept 48 months of related experience in lieu of the Bachelor's degree for a total of 84 months of related experience.

Department Preferences:

• Master's degree in a relevant field.
• Experience supporting computational and reproducible research workflows, high-performance computing, or cloud-based research environments.
• Experience supporting researchers in the health sciences or other data-intensive research disciplines.
• Knowledge of open science, reproducibility, and scholarly communication practices.
• Familiarity with AI ethics frameworks and responsible AI practices.
• Familiarity with institutional, governmental, or funding agency guidance related to AI use in research.
• Knowledge of discipline-specific data standards and repositories.
• Experience developing and delivering training or workshops.
• Background in academic or research institution settings.
• Understanding grant funding requirements (e.g., NSF, NIH data management plans).
• Proficiency in Python and/or R, Linux/Command Line, Cloud research platforms, data wrangling and transformation, data visualization.