Job Description 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.