2018-2019 StatLab Fellows

Graduate StatLab Fellows
The UVA Library’s Research Data Services is seeking multiple StatLab graduate fellows for the 2018-2019 academic year for one of two possible roles. Apply through UVA Handshake.

  1. Data Analytics Fellow: Fellows will deepen their knowledge of quantitative methods, data analysis and data science, and statistical computation by: working collaboratively with statistical and data science experts, supporting data science research on grounds through consultations with researchers across disciplinary boundaries, writing online instructional materials and articles, and developing and teaching an instructional workshop.
    • Consultations and collaborations: Consultations involve helping researchers from a variety of domains and with a wide range of experience with developing research designs, identifying appropriate analyses and models, explaining statistical methods and interpretation of results, data wrangling and statistical computation, and implementing models and algorithms in supported software environments. Fellows will join in on and eventually lead consultations.
    • Data science blog posts and articles: The Research Data Services webpage hosts a growing collection of highly-viewed StatLab posts and articles explaining common data analytic challenges, demonstrating the application of complex methods, introducing and using new computational libraries, generating effective visualizations, and more. Fellows will propose and write on multiple data science topics for the webpage.
    • Workshop instruction: The Research Data Services team in the Library supports a robust set of hands-on workshops each semester, from introductions to statistical and computing environments to instruction on advanced methods and machine learning. Workshops are free and open to all learners, and materials are archived on our webpages. Fellows will prepare and lead a data science workshop in the Spring.

    In addition, Fellows will contribute to overall technical and administrative support of the StatLab services in theLibrary.

  1. Data for Democracy Fellow: Fellows will have the opportunity to deepen their knowledge of quantitative methods, data analysis and data science, and statistical computation by: working collaboratively on a public interest data science project, contributing intellectually and methodologically; practicing open and reproducible data science workflows using tools like R, Python, GitHub, and Overleaf; communicating about the work through blog posts, research articles, and presentations.
    • Current project: The Public Presidency project works with political news data using computational text analysis to imagine and develop new ways for the public to engage political information in an increasingly complex and polarized information environment.
    • Research elements include: acquiring the text of presidential news from sources representing a range of perceived ideological perspectives; extracting features of presidential news, attention, and activity; developing a dynamic visual representation of presidentially-relevant news that citizens and researchers could use to monitor information and compare features across information streams; testing the value of such information to citizen users via experimentation; and analyzing derived data to test theories in political communication and related disciplines.
    • Project goals include: in addition to the above research goals, the project seeks to develop a set of tools and methods that could be applied to other mediated topics, and to examine how machine learning can be used to promote collective civic intelligence.

    Fellows will contribute to the project, expanding on current ideas and methods to advance the research and project goals.

Fellowship Details
Fellows in both roles have the opportunity to build their data science and quantitative knowledge, experience, and portfolios. Fellows will be paid hourly at $20/hour for up to 10 hours a week for 15 weeks each semester with the possibility of continued work over the summer.

Applicants should have completed at least a year of statistical, computational, or data science coursework before applying and be proficient in at least one statistical software environment such as R, Python, Stata, or SPSS. We particularly encourage applications from women, people of color, LGBT students, first-generation college students and other under-represented groups in the data science field.

To apply, submit an application through UVA’s Handshake system by April 30 for full consideration (applications will be considered until the positions are filled), including a CV and cover letter highlighting:

  • The StatLab fellow role that interests you, or if you’re interested in both;
  • Your experience with data analysis, statistical methods, data wrangling, and visualization on research projects or in classwork;
  • Your experience, if any, with open and reproducible tools and workflows;
  • Your research interests and what you hope to gain as a StatLab fellow.

Desired Qualifications:

  • Graduate student standing (required);
  • Experience performing statistical analyses and interpreting the results;
  • Proficiency in at least one statistical software environment;
  • Familiarity with a range of statistical methods and data analytic techniques;
  • Willingness to support academic research across a variety of domains;
  • Strong oral and written communication skills.

About StatLab and Research Data Services at UVA Library
The Library’s Research Data Services (RDS) provides expertise around data discovery and software, data management and sharing, and data analytics and use. Within RDS, the StatLab offers consultation, collaboration, training and support around data science, applied statistics, and scientific computing. We support research needs around data wrangling and cleaning, analysis and visualization, statistical inference and computational methods, reproducibility and open science. RDS/ StatLab staff collectively have expertise in open source programming languages like R and Python, in statistical environments like Stata and SPSS, and in computing technologies like Unix, GitHub, and Overleaf.

StatLab, as a service, assists with a wide portfolio of quantitative research and contributes to data science education; StatLab, as an initiative, engages in collaborative knowledge production through projects of the Data for Democracy Lab. You can learn more about the StatLab at our website.