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Gaist Solutions works with a number of organisations in the public and private sector with responsibility for managing large infrastructure portfolios, primarily in the highways and emergency services sectors. Our clients are facing unprecedented challenges to manage their infrastructure assets with decreasing budgets and growing risks, in particular, from climate change. This is not just about managing conflicting priorities in the present but also ensuring that future generations are not left with a legacy of declining infrastructure.

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Gaist Solutions are seeking to build a team of talented data analysts to support our clients in meeting these challenges. The roles will involve the use of a range of skills to derive intelligence from large spatial and time series data sets on infrastructure performance and risk events and develop predictive models for testing investment strategy options.

At a basic level the roles will involve desk-based collation, cleaning and querying of GIS based data with summary analysis and report writing. This will include basic tests of statistical significance and identification and investigation of outliers. Dependent on skills and experience, the analysts will move towards working with more complex relational and non-relational data base structures using appropriate querying methods and then towards use of advanced methods for statistical inference, testing and predictive modelling. This will include exploratory analysis to identify appropriate model forms and testing of alternative models.

Successful candidates will be joining a friendly and growing team based in our offices at University of Lancaster. Our ethos is to encourage active participation of all our team members in sharing new and innovative  ideas to solve problems and meet high level objectives.

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Essential criteria

An Undergraduate or Post-graduate qualification is essential. However, we would welcome applications from candidates from a wide range of potential academic disciplines where there is a substantial practical element involving the use of statistical methods for inference or predictive purposes. As such, appropriate disciplines are likely to include (but are not restricted to):

• Geography (human or physical)

• Engineering or material sciences

• Life sciences including Ecological or Biomedical sciences

• Economics or business and finance

• Social or political sciences

Applicants should be able to demonstrate that their use of statistical or computational methods within their discipline extended beyond occasional practical lab sessions as part of their course and that they were motivated to use these for part of their final year project or dissertation or they have since applied these methods in their work experience during or after completion of their study.

Applicants must be able to demonstrate an enthusiasm for learning new skills and knowledge. We do not expect applicants to have a full grasp of all of the practical skills that will be required as part of the role. However, successful candidates will be expected to continually develop these skills and industry knowledge and we will establish clear learning objectives with appointees.

Applicants must also demonstrate that they are self-motivated and can work with minimal supervision. They must be willing to work in a mutually supportive team and they must also be willing to be flexible to work varying hours as needed to meet deadlines with according time off in lieu.

Appointments will be subject to a 3 month probationary period.