Graduate StatLab Fellows
The UVA Library’s Research Data Services is seeking multiple StatLab graduate fellows for the 2019-2020 academic year.
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, developing and teaching an instructional workshop, working on library data science projects as they arise. In addition, Fellows will practice open and reproducible data science workflows using tools like GitHub and Overleaf and contribute to overall technical and administrative support of StatLab services in the Library.
- Consultations and collaborations: Consultations involve helping researchers from a variety of domains and with a wide range of experience in 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 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 contribute posts 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 have the opportunity to prepare and lead a data science workshop in the Spring.
- Library data science projects: The Library itself has increasing needs for data science support to analyze and understand our own data. StatLab contributes to these projects and Fellows will be brought in as new projects develop.
Fellows 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.
- Graduate student standing (required);
- Experience performing statistical analyses and interpreting the results;
- Proficiency in at least one statistical software environment, interest in and willingness to learn others;
- Familiarity with a range of statistical methods and data analytic techniques, interest in and willingness to learn others;
- Interest in supporting academic research across a variety of domains;
- Strong oral and written communication skills.
You can view the position on Handshake and WorkDay (R0004522). Please apply by April 19 for full consideration, including a CV and cover letter highlighting:
- Your experience with data analysis, statistical methods, data wrangling, and visualization on research projects or in classwork;
- Your experience with statistical and computational research tools, including open and reproducible tools and workflows;
- Your research interests and what you hope to gain as a StatLab fellow.
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, StatLab assists with a wide portfolio of quantitative research and contributes to data science education through consultations, collaboration, and training around data science, applied statistics, and scientific computing; common challenges and needs include 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/LaTeX. You can learn more about the StatLab at our website.