Data science associate

• End-to-end analytical ownership from data analysis and model development to implementation and ongoing enhancement, alongside improving processes and supporting optimisation initiatives
• Contribute to the evolution of the analytics platform, including the practical application of AI and advanced techniques
• Support portfolio management across credit risk, balance sheet optimisation, and capital efficiency by delivering data-driven insights and analytical tools, including for SRT and transaction activity
• Develop and maintain models, improve processes, and apply advanced techniques to enhance decision-making and portfolio optimisation
• Partner with internal Structuring, Treasury and Middle Office to execute new structured transactions, as part of a deal team
• Ongoing management and optimization of the structured transactions, partnering with internal teams and external parties such as law firms/verification agents, rating agencies and agent banks
• Together with relationship/Sales teams, support with investor engagement including queries related to existing and prospective transactions
• Support of wider portfolio optimization initiatives as well as analytics, returns and model enhancements

• Strong quantitative background (e.g. Mathematics, Physics, Engineering, Economics) with exposure to statistical or financial modelling through study or early experience
• Proficiency in Python (e.g. pandas, NumPy) and working knowledge of SQL, with a solid understanding of data structures and data quality considerations
• Basic understanding of credit risk concepts (PD, LGD, EAD, RWA), or the ability to learn them quickly
• Strong problem-solving skills, with the ability to write clear, well-structured code and an interest in developing robust coding practices and production-ready solutions
• Proactive, independent working style with good communication skills, and the ability to clearly explain analytical outputs and collaborate effectively

• Exposure to portfolio analytics, credit risk, securitisation or balance sheet management concepts
• Experience working with financial datasets or relevant coursework/projects
• Awareness of working in a regulated environment or interest in developing this understanding
• Familiarity with basic machine learning or statistical techniques
• Exposure to, or interest in, AI techniques and their application in financial analytics or workflow optimisation

Hybrid working (anchor days at onsite location)