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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