Scientific Software Engineer - UK Applied Science

at  Met Office

Job Introduction
We’re looking for an exceptional Scientific Software Engineer help us make a difference to our planet.
As our Scientific Software Engineer, the job may be suitable for hybrid working, which is where an employee works part of the week in the office and part of the week from home. This is a voluntary, non-contractual arrangement and the location advertised will be your contractual place of work.
Our opportunity is full time, 37 hours per week, but we would also consider applicants wishing to work a minimum of 30 hours per week and we will also try our best to consider those intending to work a job share. Our people are at the heart of what we do and we’ll do our best to agree a working pattern that works for everyone.

ESSENTIAL CRITERIA, SKILLS AND EXPERIENCE:

• Show technical insight through delivery of significant advances in software capability by the effective application of technical knowledge, understanding and skills (We’re Experts by Nature)
• Champion and ensure good Quality Assurance processes, best practice, standards and/or regulations in your work and the wider team, including performing user testing on products prior to launch (We Live and Breathe it)
• Engage with others to understand the technical and scientific requirements for your work and its wider context, and work with colleagues to implement scalable data science / spatial risk products (We’re Better Together)
• Communicate knowledge accurately and concisely in written documents and group discussions, present analysis and visualisations tailored to your audience and building trust / credibility (We’re Better Together)
• Proactively develop your skills and support others in developing their skills, leading to beneficial impacts and efficiencies (We Keep Evolving, Better Together, Experts by Nature)

• Develop, build and optimise risk assessment software platforms from different sectors for use in seasonal, decadal and climate risk assessment and decision making.
• Develop and deploy machine learning approaches to hazards and impacts modelling to better integrate such methods across the project the team is working on and within those at the proposal stage.
• To champion software development best practise and build such capabilities within the team.
• Support the implementation of aspects of operational software tools for use with climate prediction and projections