Data 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 thrive in this role, you should possess a strong foundation in data science and a passion for responsible AI
• Strong Communication Skills: Demonstrated ability to communicate complex technical concepts clearly and effectively to non-technical stakeholders, with a passion for explaining data science products and insights
• End-to-End ML Lifecycle Ownership: Experience developing and deploying complex machine learning models, owning the entire lifecycle from exploratory data analysis to production monitoring
• MLOps Familiarity: Familiarity with GCP's MLOps suite including Vertex Pipelines (or similar platforms)
• Technical Proficiency: in SQL and Python, along with familiarity with standard libraries used in data science such as scikit-learn, Keras, TensorFlow, pandas, NumPy, seaborn, and Matplotlib

Desirable

• Our Data teams are aligned to divisions covering the following Banking Services & Products, Customer Identity & Financial Crime and Data & ML Engineering
• Our Data teams are excited about delivering meaningful and impactful insights to both the business and more importantly our customers. Hear from the team in our latest or our case studies with
• As a Data Scientist in the Machine Learning Observability & Governance team, you will play a crucial role in enabling Starling Bank to maximally exploit AI in line with its risk appetite, while ensuring ethical and responsible AI practices
• Empowering Decision-Making through Education: Educate model owners and other stakeholders on data science products and concepts, to empower them to make high-quality decisions about the models for which they are responsible
• Stakeholder Communication & Visibility: Ensure clear communication and good visibility with stakeholders such as risk teams, regarding how data scientists at Starling observe and manage ML and AI models
• Observability Centre of Excellence: Support colleagues in enhancing their observability work by maintaining existing observability tooling, assisting in identifying key metrics to monitor, and providing expert advice on internally-developed model behaviour characterisation techniques
• Innovate and Implement Novel Methods: Develop and implement cutting-edge techniques and frameworks to deepen our understanding of AI and ML model behaviour, enabling the safe and effective exploitation of advanced AI. This includes contributing to initiatives such as LLM-as-a-judge, RAG evaluations, and agentic workflow assessment

• 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