Machine Learning Applied Scientist, Starling Bank

• We are looking for talented data professionals at all levels to join the team
• We value people being engaged and caring about customers, caring about the code they write and the contribution they make to Starling
• People with a broad ability to apply themselves to a multitude of problems and challenges, who can work across teams do great things here at Starling, to continue changing banking for good
• To succeed in this role, you should possess a strong academic foundation and practical experience in advanced machine learning and AI
• Research Experience: Demonstrated experience in a research-focused role, either in academia or industry, with a track record of contributing to or leading research projects
• Industry Production Experience: Proven experience managing and deploying advanced ML/AI models in a production environment, understanding the complexities of real-world systems
• Technical Proficiency: Strong programming skills in languages commonly used in ML and (e.g., Python), and familiarity with relevant libraries and frameworks

Desirable

• As a Machine Learning Applied Scientist in the Machine Learning Observability & Governance team, you will be instrumental in advancing Starling Bank's capabilities in safely and effectively exploiting AI and ML technologies
• Pioneering Novel Methods: Lead the development and implementation of innovative research-driven methods to deepen our understanding of AI and ML model behaviour in a production banking environment
• Translating Research to Production: Bridge the gap between research and practical application by taking external knowledge (e.g., from preprints, conferences) and transforming it into working code and frameworks for production systems, ensuring scalability and maintainability
• Knowledge Dissemination: present research findings both internally and externally (e.g., at conferences or through publications), to share advancements and foster a culture of continuous learning within the data practice
• Collaborative Innovation: Work closely with data scientists, ML engineers, and other stakeholders to identify key challenges in ML observability and governance, and propose research-backed solutions

• Interviewing is a two way process and we want you to have the time and opportunity to get to know us, as much as we are getting to know you!
• Our interviews are conversational and we want to get the best from you, so come with questions and be curious
• In general you can expect the below, following a chat with one of our Talent Team
• Stage 1 - 30 mins with one of the team
• Stage 2 - Take-home challenge
• Stage 3 - 60 mins technical interview with two team members
• Stage 4 - 45 min final with an two executives