Data Specialist
UNC-Chapel Hill
On-site
Chapel Hill, North Carolina, United States
Data Specialist
Position Summary
The Data Specialist at the UNC -Chapel Hill University Libraries will provide artificial intelligence (AI) and data support for a project funded by the Mellon Foundation called On the Books: AI-Assisted Archives. Previous phases of this project created textual datasets of Jim Crow-era session laws from North Carolina, South Carolina, and Virginia and used machine learning to identify likely Jim Crow laws. The next phase of the project will expand this work to include the state of Texas and will endeavor to identify both Jim Crow and Juan Crow laws. Additionally, the project will investigate broadly the potential use of various applications of artificial intelligence for archival research. Examples include creating text corpora from historical legal documents, generating descriptions of photograph collections, and performing handwritten-character recognition ( HCR ) on archival documents. The Data Specialist will be a member of the Digital Research Services department at the University Libraries as well as a member of the On the Books project team. The On the Books project team will use code and AI tools to create corpora and metadata and use machine learning to identify Jim Crow and Juan Crow laws. The Data Specialist will collaborate with scholars and special collections team members to ensure the work is applied appropriately for legal and historical contexts. The project team is committed to mentorship and maintaining a growth mindset. This is an excellent opportunity for someone to develop their skills in coding and AI. This position may be eligible for a hybrid work arrangement to include a partially remote work location, consistent with System Office policy. UNC Chapel Hill employees are generally required to reside within a reasonable commuting distance of their assigned duty station.
Required Qualifications, Competencies, And Experience
An advanced degree in Information Science, Data Science, Statistics, Computer Science, or related field. Proficiency working with unstructured data in Python and/or R. Demonstrated advanced data skills, including data cleaning/wrangling/normalization, using regular expressions, and reshaping/merging data. Proficiency using tools and programming libraries to support text analysis. Some exposure to generative AI and a strong interest in learning more. Experience working effectively with a team to plan and complete projects. Detail oriented with excellent communication skills.
Preferred Qualifications, Competencies, And Experience
Experience using Python and/or R and relevant libraries for working with images, natural language processing, and working with application programming interfaces (APIs). Knowledgeable about AI, including prompt engineering. Experience using version control. Experience developing technical documentation. Experience working with data for projects in the social sciences and/or humanities. Experience using AI tools for processing data. Experience in building, training, evaluating, and tuning predictive models.
The Data Specialist at the UNC -Chapel Hill University Libraries will provide artificial intelligence (AI) and data support for a project funded by the Mellon Foundation called On the Books: AI-Assisted Archives. Previous phases of this project created textual datasets of Jim Crow-era session laws from North Carolina, South Carolina, and Virginia and used machine learning to identify likely Jim Crow laws. The next phase of the project will expand this work to include the state of Texas and will endeavor to identify both Jim Crow and Juan Crow laws. Additionally, the project will investigate broadly the potential use of various applications of artificial intelligence for archival research. Examples include creating text corpora from historical legal documents, generating descriptions of photograph collections, and performing handwritten-character recognition ( HCR ) on archival documents. The Data Specialist will be a member of the Digital Research Services department at the University Libraries as well as a member of the On the Books project team. The On the Books project team will use code and AI tools to create corpora and metadata and use machine learning to identify Jim Crow and Juan Crow laws. The Data Specialist will collaborate with scholars and special collections team members to ensure the work is applied appropriately for legal and historical contexts. The project team is committed to mentorship and maintaining a growth mindset. This is an excellent opportunity for someone to develop their skills in coding and AI. This position may be eligible for a hybrid work arrangement to include a partially remote work location, consistent with System Office policy. UNC Chapel Hill employees are generally required to reside within a reasonable commuting distance of their assigned duty station.
Required Qualifications, Competencies, And Experience
An advanced degree in Information Science, Data Science, Statistics, Computer Science, or related field. Proficiency working with unstructured data in Python and/or R. Demonstrated advanced data skills, including data cleaning/wrangling/normalization, using regular expressions, and reshaping/merging data. Proficiency using tools and programming libraries to support text analysis. Some exposure to generative AI and a strong interest in learning more. Experience working effectively with a team to plan and complete projects. Detail oriented with excellent communication skills.
Preferred Qualifications, Competencies, And Experience
Experience using Python and/or R and relevant libraries for working with images, natural language processing, and working with application programming interfaces (APIs). Knowledgeable about AI, including prompt engineering. Experience using version control. Experience developing technical documentation. Experience working with data for projects in the social sciences and/or humanities. Experience using AI tools for processing data. Experience in building, training, evaluating, and tuning predictive models.