Help shape research agendas that apply data science and digital humanities to archival collections while centering community engagement, ethical stewardship, and the human stories embedded in the data.

Responsibilities

• Lead new technical innovations for working with datasets on CBDR projects that include developing new quantitative and qualitative techniques, concepts, or approaches to datasets.
• Lead or co-lead new efforts to explore, understand, and present new approaches to applying machine learning, large language models, or other technologies that will address current technical challenges, advance the CBDR’s work with data and digital collections, and are in-line with CBDR principles.
• Problem solving moderate to complex problems, applying expertise and practices of the field to propose solutions and their benefits, limitations, impact, and how they can map onto the CBDR’s multi-layered digital ecosystem.
• Analyze system pipelines, workflows, and software options and propose solutions to ensure effectiveness, scalability, and interoperability of computing and digital publishing infrastructures that bridge interdisciplinary gaps and streamline cross-disciplinary project work.
• Contribute to maintenance, sustainability, and documentation of the CBDR digital infrastructures, computational applications, and related software.
• Organize, assign, and review completed work to verify accuracy, quality, and adherence to professional standards.
• Share and demonstrate expertise at venues (conferences, training programs, publications) to contribute to advancing CBDR scholarly research agendas and elevating the profile of Black public and digital humanities and transformational technologies.
• Collaborate/strategize with colleagues in Special Collections and across colleges to consider critical questions and ethics of digital access, engagement, and exploration of archival material particularly concerning the use of artificial intelligence (AI) systems with archives of marginalized and underrepresented communities.
• Lead or co-lead presentations, workshops, and trainings to expand understanding of ethical use of machine learning and the archives, and think about the connectedness of the work and where there are opportunities not visible in traditional academic structures.
• Supervise part-time team members (e.g., graduate and undergraduate students) as the need arises.

Skillset

• Experience with critically applying computational methods to the process of collecting, refining, analyzing, and interpreting complex datasets.
• Outstanding problem-solving skills.
• Effective written and oral communication skills, with the ability to tailor messages to different audiences and contexts.
• Inclusive and respectful when working with others.
• Versed in best practices in the fields of digital humanities, computer and information sciences, data science, and/or machine learning.
• Proficiency in Python.

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