The Research Data and Digital Scholarship team currently boasts expertise in text mining, Geographic Information Systems and spatial visualization, data curation, topic modeling, network analysis, data stewardship, public digital humanities, research data management and FAIR data, and research communication. The team provides training in digital literacies, ethics, and methods to students, faculty, and clinicians; collaborates and advises on the design, creation, dissemination, and preservation of digital scholarship projects. The Applied Data Science Librarian will unique but complementary strengths, including but not limited to in-depth knowledge of programming and scripting languages and libraries for computational research at all levels to an engaged team and play a critical role in the disciplinary expansion of the team’s deep expertise in the humanities and humanistic social sciences and experience in meeting researcher needs to the quantitative social sciences, the biomedical sciences, and beyond.
Qualifications
ALA-accredited master’s degree in Library or Information Science or advanced degree in computer science, a quantitative social science, or related field.
Ability to use a variety of tools to extract and manipulate data from various sources (such as relational databases, web services and APIs).
Demonstrated experience with programming languages such as JavaScript, R, and Python and libraries for data visualization and machine learning (e.g., Plotly, Matplotlib, SciKit-Learn, Pattern)
Demonstrated advanced data skills, including data cleaning/wrangling/normalization, using regular expressions, and web scraping.
Familiarity with one or more data visualization tools or programming libraries (e.g., Tableau, d3.js, ggplot2, R Studio)
Demonstrated experience with data analysis tools such as R, STATA, SPSS, and SAS.
Experience with the creation, dissemination, and teaching of interactive instructional materials via Jupyter Notebooks and containerized environments
Interest in the ethical procurement, structuring, documenting, and interpreting of data for AI/ML
Interest in algorithmic bias and the responsible use of data science and machine learning for research and scholarship
To apply for this job email your details to agate@upenn.edu