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L. Angie Ohler

Associate Dean for Content and Digital Initiatives
University Libraries

Office: 479-575-5973
https://orcid.org/0000-0002-3832-7573

MULN 212A | 365 N. McIlroy Ave.
University of Arkansas | Fayetteville, AR  72701-4002

[email protected]<mailto:[email protected]>

libraries.uark.edu<http://libraries.uark.edu/>

On Jan 6, 2021, at 12:54 PM, Code4Lib Jobs <[email protected]> wrote:




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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