9 months contract with a Local Authority

Job Summary:

• The Met Office, in partnership with the Alan Turing Institute, is developing a novel data-driven forecasting system that leverages proprietary Met Office data and recent advances in AI and machine learning for earth system modelling. A prototype atmospheric forecast model, ‘FastNet’, has achieved comparable performance to our Global Numerical Weather Prediction (NWP) system on some key measures of accuracy, and with further development has the potential to exceed the performance of traditional NWP systems, revolutionising the production of operational weather forecasts at the Met Office by offering faster, more efficient, and more accurate forecasts to help people stay safe and thrive.
  
• The focus for this role will be adding technical leadership to complement existing scientific leadership to an existing team of software engineers and data scientists. The role will support the existing project manager to lead on effective agile delivery practices. Further, the role will build technical understanding of the challenges around this machine learning activity and develop, and monitor against, a technical roadmap to address them. This role will need to balance the need for scientific progress with sustainability, maintainability, long-term delivery and improving the development cycle time. Contributing high quality code and reviews to the project as well as mentoring and developing junior members of the team will be part of the role.  
  

Key Duties/Accountabilities (Sample):

• Supported by the project manager, act as Scrum Master and facilitate the delivery team to work effectively. 
  
• Lead the development of technical plans and roadmaps for the FastNet capability 
  
• With the assistance of the development team and project manager monitor progress against and adapt roadmaps escalating via the project manager when this effects milestones/deliverables. 
  
• Assist, mentor and develop team members; build capability and capacity for the team. 
  
• Respond to pull requests; review and refactor prototype science code for efficiency and robustness  
  
• Work as part of a team to incorporate new scientific developments into the FastNet code base.  
  
• Review and promote coding best practices for the project, including use of appropriate tools to facilitate this.  
  
• Maintain good documentation and promote knowledge transfer to other team members through pair programming, coaching, and team discussions. 
  

Skills/Experience:

• Expert knowledge of Python, knowledge of quality assurance with Python, especially testing and documentation.
  
• Expert knowledge of agile development practices, specifically the Scrum framework.
  
• Knowledge of developing and deploying machines learning workflows on cloud platforms such as AzureML.
  
• Knowledge of working with large structured and unstructured datasets, ideally geospatial data.
  
• Ability to mentor and develop others.
  
• Show deep and/or broad relevant technical insight through facilitation of significant advances in software capability by the effective application of technical knowledge and interpersonal skills. This should include the development of sophisticated machine learning models.
  
• Promote good Quality Assurance processes, best practice, standards and/or regulations in your work and that of others.
  
• Communicate knowledge accurately and concisely in written documents and discussions in groups, tailoring communication for diverse audiences, and actively engage with others to understand the requirements and wider context of your work.
  
• Mentor, coach, train and support others in developing their technical skills, leading to improved output of individuals and the team and the career development of others.