Senior Scientific Software Engineer

Senior Scientific Software Engineer

Exeter

Job Role

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 Responsibilities

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.

Key skills

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 machine 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