A typical day will look like this
- Work within a team to deliver end-to-end technical solutions — typically starting with spike sessions, onto architectural design and test creation, iteration on the solution, measuring quality and ultimately deploying to production.
- Participation in the design and scoping of greenfield projects
- Commitment to software best practices and a strong culture of peer review.
Skills
We're after exceptional candidates, who have real world experience but are eager to learn.
Essential:
- Demonstrated history working in a numerical field: e.g. computer vision, applied maths, physical sciences, geospatial analysis.
- Strong approach to systems thinking, whilst remaining pragmatic
- Commitment to software engineering principles for scientific python, a keen eye for clean code, and a passion for robustness and correctness.
- Working on shared codebases to produce production quality code.
Highly Desirable:
- Working with large data sets, where data doesn’t fit into memory, and requires multiple nodes to compute efficiently.
- A scientific mindset of formulating hypotheses, and applying statistical tests to validate them.
- Working in a cloud-native environment using highly scalable compute.
- Experience with operationalizing numerical applications and workflows.
Personal attributes
- Data science is a team sport; communicate well, share knowledge, and be open to taking on ideas from anyone in the team.
- While extensive knowledge of theory and best practices are highly valued, pragmatism wins over elaborate theory when it comes to shipping products that work.
