Data Scientist, Defence and National Security

• Understand the problems of the Turing's partners and develop appropriate approaches to solving these problems.
• Perform experiments and develop capabilities, which might include building and deploying machine learning models; applying data science, statistical and algorithmic techniques to data; building microservices, data processing/engineering systems and platforms or developing user interfaces and/or visualisations.
• Develop, implement and adapt state-of-the-art and novel data science and artificial intelligence techniques emerging from the Institute and elsewhere to problems faced by the Turing's partners.
• Present, disseminate and explain our work including documenting capabilities, processes, and systems for effective and efficient reuse across multiple domains; presentation at Defence and Security programme events including monthly meetups and wider Turing events; presentation at Partner reading groups, conferences and to Partner stakeholders; publication, support and maintenance of research/prototype software.

This role contributes to cutting-edge research and engineering approaches to solve real-world problems in AI/ML security, working in close collaboration with Government partners to bolster defence and national security capabilities.

• Eligibility for Developed Vetting (DV) clearance and a willingness to undergo the DV clearance process once in post, if not already held.
• A PhD degree or equivalent professional experience in a field with significant use of both computer programming and advanced algorithmic, statistical or numerical techniques.
• Undergraduate-level degree or higher in computer science, data science, mathematics, physics, statistics, software engineering or a related-discipline.
• Professional experience in a field or sector with significant use of both computer programming and advanced algorithmic, statistical or numerical techniques.
• Fluency in one or more modern programming languages used in data science. We predominantly work in Python, but demonstrable use of other programming languages (e.g. modern C++/C#, Java, Scala, Julia, R, Javascript, Rust, Go) together with a facility for learning new languages.
• An understanding of the importance of good practices for producing reliable software and reproducible analyses (e.g. version control, issue tracking, automated testing, package management, literate analysis tools such as Jupyter).
• Experience working with customers to identify, understand and refine problems, scoping data science work to solve them.

No specific benefits, compensation, or perks mentioned in the job description.