Data Scientist, Center Black Digital Research

Position: Data Scientist, Center for Black Digital Research
#
** APPLICATION INSTRUCTIONS:*** ## CURRENT PENN STATE EMPLOYEE (faculty, staff, technical service, or student), please to complete the. Please do not apply here, apply internally through Workday.* ## If you are NOT a current employee or student, please click “Apply” and complete .###
** Approval of remote and hybrid work is not guaranteed regardless of work location. For additional information on remote work at Penn State, see .
** This position is funded for 3 year(s); continuation past 3 year(s) will be based on university need, performance, and/or availability of funding.###   
** POSITION SPECIFICS
** The Pennsylvania State University Libraries seek a creative and forward-thinking
** Data Scientist
** to advance computational research and digital scholarship using archival collections and datasets.
** Position Overview
** This position is based in the Eberly Family Special Collections Library (SCL) and collaborates closely with the Center for Black Digital Research (CBDR) and its interdisciplinary faculty directorate to support the stewardship and computational exploration of archival collections within the SCL while advancing research initiatives and digital scholarship projects through CBDR.The
** Eberly Family Special Collections Library (SCL)
** is a community-centered cultural heritage repository that supports teaching, learning, and research through meaningful engagement with distinctive collections.

SCL places student success at the center of its work and serves as a laboratory for learning and discovery where students, faculty, and researchers interact directly with primary sources to generate new knowledge and scholarship. Through ethical stewardship, community partnership, and digital innovation, SCL connects Penn State, Pennsylvania, and the wider scholarly world with the stories preserved in its collections.

Within this environment, the
** Data Scientist
** will contribute to emerging work at the intersection of archives and critical approaches to artificial intelligence, machine learning, and digital humanities.  The
** Center for Black Digital Research (CBDR)
** is a public-facing, digital research center and a collaboration between the University Libraries, the College of the Liberal Arts, and the College of Engineering. CBDR advances what it describes as transformational technologies: the intersection of critical approaches to artificial intelligence, machine learning, and digital humanities methods applied to Black cultural heritage and digital collections.

The
** Data Scientist
** will be responsible for developing innovative computational approaches to working with digital collections and datasets, including machine learning, large language models, data visualization, and other emerging methods.

This position will lead training and mentorship across interdisciplinary teams of students, faculty, and staff on data workflows across multiple projects and areas of research.

As a key member of the CBDR team, the
** Data Scientist
** will 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.

The
** Data Scientist
** will contribute technical expertise, leadership, and collaborative research support for projects involving digital collections, computational analysis, and Black digital humanities scholarship.
** More specific responsibilities include but are not limited to**:
* 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 .
* 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…