4 September 2019

Kew, London


Fixed Term


The National Archives is the official archive of government for England, Wales and the UK and leads the wider UK archives sector. The achive is custodian for some of our most iconic national documents which date back over 1,000 years. The National Archives work to secure the future of the Public Record, both digital and physical, for future generations. With an ever-increasing collection of born-digital records, finding the most effective ways to preserve that heritage is vital to ensure the nation’s memory survives. As an Independent Research Organisation, The National Archives carries out innovative research which leads archival practice across the sector.

About the role

An exciting opportunity has arisen to develop your statistical career in one of the most fascinating, dynamic and intellectually stimulating organisations in the cultural sector. You have the chance to participate in a research project to build a structured evidence base for Digital Preservation.

As a Research Assistant in the Digital Archiving Department, you will work alongside the project team at The National Archives and collaborate closely with partners in the Applied Statistics and Risk Unit at the University of Warwick.

You will elicit specialist knowledge from archival professionals, build a Bayesian statistical model and help to embed a risk-aware approach to digital preservation at The National Archives and archival partners.

About you

An expert in Bayesian statistical methods with the ability to build a structured evidence base in R, you should have an active research profile and a good network in the academic and research sectors related to statistical analysis.

Your communication skills, with academics and non-technical people, will be excellent, with the ability to interact well and build effective relationships and within the organisation and externally at training events to disseminate the project’s findings.

Person specification


  • Strong evidence of research in Bayesian methods; a higher degree in a subject including Bayesian Statistics, a personal published track record in applied Bayesian statistics or a demonstration of significant equivalent experience.
  • Strong evidence of computational expertise in using complex statistical and other related software packages, including R.
  • Strong communication and relationship building skills; able to lead and facilitate work to gather evidence from the business and to explain technical concepts clearly to non-technical staff.
  • Excellent interpersonal skills, especially an ability to work openly and collaboratively in multi-disciplinary teams, including with colleagues without a statistical background.
  • Highly motivated to work independently and as a project team member. Ability to prioritise tasks and deliver products to agreed quality and timeframes.
  • Baseline security clearance or willingness to undergo clearance.


  • Familiarity with source code repositories such as GitHub.
  • A knowledge of the principles and practice of digital preservation.
  • Familiarity with expert elicitation techniques such as the IDEA protocol or the Delphi method


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