Job Introduction
We’re looking for three exceptional Scientific Software Engineers - Data Systems to help us make a difference to our planet.
As our Scientific Software Engineer - Data Systems, 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 20 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.

YOUR WORLD OF EXPERTISE

Data is a key part of achieving real-world impacts through our climate research and weather forecasts. It is increasingly the differentiator in the machine learning (ML) revolution in weather and climate modelling.
We are advertising multiple scientific software engineer (SSE) job opportunities in scientific data engineering to help us make a big difference in these areas, from the next iteration of international climate model intercomparisons (CMIP) and other climate initiatives, to processing our weather forecast data at huge scales, to maximising the benefit and impact of data for the new wave of research into ML forecasting and climate modelling.
You will be a key part of enabling and pulling through world-class science, with a fully featured scientific software engineering career path.

There are three teams advertising posts:

• The Data Delivery team is at the heart of getting much of our climate modelling data out to the global research community, industry, and others. It is responsible for developing, maintaining, and supporting the use of the Climate Data Dissemination System (CDDS). CDDS is a suite of Python code and cylc workflows primarily developed to support the delivery of large volumes of standardised climate model output to international activities such as the Coupled Model Intercomparison Project (CMIP). Development over the last few years has made it possible to apply the same processes to model output for other projects and we are looking for SSEs to assist in the further generalisation and adaptation to new projects as we approach the next round of CMIP.
• The ML Datasets team was launched in response to the amazing revolution in data-driven ML forecasting over the last 2-3 years. The Met Office has recognised the need to build on its strength in data creation and invest in training datasets to contribute to this field, in flagship programmes for weather and climate (AI4NWP, AI4Climate). The datasets we need are diverse and include real and simulated observations, socio-economic data, regional modelling hindcasts, third-party observations, and reanalysis data to CMIP6 projections (working with the Data Delivery team!). The team will be involved in developing and scoping technology to create, manage, and deploy these across a range of platforms, and in prototyping their use.
• The Verification, Impacts, and Post-Processing Systems team manages the processing and delivery of weather forecast data to get our many terabytes of weather forecast data per day useful, usable, and used, working closely with scientists. The team create and maintain a variety of Python based workflows (notably StaGE and IMPROVER) for standardising model data and combining multiple data sources to add value to the Met Office weather forecast products. The post in this team will play a key role in adapting to our next generations of physical and ML based models at unprecedented scale, including preparing changes to our 24/7 operations.

ESSENTIAL CRITERIA, SKILLS AND EXPERIENCE:

1. Technical Skills. Evidence of showing technical insight through delivery of significant advances in software capability. (value: Experts By Nature)
2. Data Skills. Evidence of a keen interest in high quality datasets and experience in working with data to achieve beneficial impacts, preferably scientific or geospatial. (value: Live and Breathe It)
3. Understanding Requirements. Evidence of engaging with others to understand the requirements for your work and its wider context, and contributing to the development of plans to meet these requirements. (value: Better Together)
4. Quality Assurance: Evidence of applying good Quality Assurance processes, best practice, standards and/or regulations in your work. (value: Experts by Nature, Live and Breathe It)
5. Communication. Evidence of communicating knowledge accurately and concisely in written documents and group discussions, tailoring communication for diverse audiences and building trust / credibility. (value: Better Together)
6. Skills Development. Evidence of proactively developing your skills and supporting others in developing their skills, leading to beneficial impacts. (value: We Keep Evolving)

• Deliver meaningful advances in capability for colleagues and/or stakeholders through the effective application of scientific software engineering knowledge, understanding and skills.
• Provide proactive scientific and technical insight into the solving of scientific software engineering tasks or problems.
• Engage with others to understand the requirements for your work and its wider context, and contribute to the development of plans to meet these requirements.
• Apply good Quality Assurance processes, best practice, standards and/or regulations to all aspects of your work to achieve the agreed standard.
• Communicate accurately and concisely with colleagues and wider stakeholders to develop trust and credibility, tailoring your method of sharing to a range of audiences
• Develop your skills proactively and provide support to others to develop their skills leading to beneficial impacts.