Develop the Graphical User Interface of an existing shiny application in R, working iteratively to deliver new functionality and an improved user interface using expert knowledge to improve the usability of the application through front-end development to meet GDS service standards and Web Content Accessibility Guidelines (WCAG) 2.1 level AA.
Who you will work with
You will work with a multidisciplinary project team working on a new decision support tool based upon a probabilistic graphical model of risk to digital archives.
You will work with the Service Owner for Digital Preservation (the project lead) and with a Research Assistant in Applied Statistics, who has been maintaining and deploying the application since the original developers completed the first version.
You will also discuss ideas and feedback on an ad hoc basis with a Senior Digital Archivist, a User Researcher and Statisticians from the University of Warwick.
What you will do
An experienced front-end developer is required to improve the graphical user interface of an R shiny application. This is a decision support tool which allows archivists to assess risk by changing parameters and seeing how different policies or scenarios alter their risk score. The underlying methodology for quantifying this risk is a Bayesian statistical network.
Skills and Experience Required
Essential skills and experience
- Have strong experience of developing applications in R using shiny and other supporting packages (or similar technologies, please specify).
- Have experience of User Interface development for a web-based application including interactive graphs and visualisations.
- Have the ability to test components and pages against accessibility standards and fix accessibility issues identified through testing.
- Write clean, accessible and performant code that it is open by default and easy for others to re-use.
- Work in an Agile way, following a test-driver approach and demonstrating progress and discussing findings regularly.
Nice-to-have skills and experience
- Have experience of coding in R.
- Have a working knowledge of Git for version control.
- Have a working knowledge of performance optimisation.
- Have a background in statistics or analysis.
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