The Applied Data Science Services Librarian provides robust data science support services and evidence-based pedagogical opportunities in computational methods for Penn community members across the disciplines and with varying degrees of technical and methodological experience. The Librarian designs and delivers a sustainable, scalable research support program and a range of instructional support materials for established and emerging data science tools and methodologies for such as web scraping, data mining, and machine learning, as well as programming and scripting languages such as Python and R for research, analysis, and data visualization and communication. Along with colleagues in Research Data and Digital Scholarship, the position is responsible for providing instruction and outreach to faculty, students, and interdisciplinary campus groups, supporting both individual and team or lab data-driven research and scholarship.

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


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