We are working with a growing consumer finance fintech platform, who are looking to build out their Data and Analytics department. The team, who have recently received backing from a global Investment firm are now building out a Data and Analytics team in London - with a focus on supporting their Unsecured NPL Portfolio Investment team.
This role is ideal for a technically strong, quantitatively minded professional with a background in statistics and data science, looking to apply their skills in financial services. While specific NPL experience is not required, a passion for large-scale data analysis and modern analytics frameworks is essential.
You will work in a neo-bank/fintech-style environment, using cutting-edge technology to analyze terabytes of data and support high-impact investment decisions.
Responsibilities:
- Develop, implement, and validate statistical and machine learning models for analysing non-performing loan portfolios.
- Collaborate with cross-functional teams—including credit, collections, and data engineering—to translate business objectives into robust analytical solutions.
- Build software using modern technology to enable investing and asset management at scale.
- Apply Bayesian modelling and probabilistic programming techniques to address uncertainty and improve prediction accuracy.
- Analyse large-scale datasets to identify key drivers, trends, and early warning signals within NPL portfolios.
- Clearly communicate model results, insights, and recommendations to stakeholders, including both technical and non-technical audiences.
- Stay current with advances in statistical modelling, machine learning, and data science, continuously evaluating and integrating new techniques and tools.
Requirements:
- University degree in a STEM field (e.g., Mathematics, Statistics, Computer Science, Engineering, Physics, Economics); advanced degree preferred.
- Strong expertise in statistical modelling, Bayesian inference, and machine learning.
- Proficient in Python (using libraries such as NumPy, pandas, scikit-learn, PyMC or Stan)
- Experienced in SQL. Ability to write efficient and robust queries.
- Demonstrated experience working with large and complex datasets.
- Ability to communicate complex analytical concepts clearly and effectively to a range of audiences.
- Experience with model governance, documentation, and deployment best practices.
- Experience with cloud environments (e.g., AWS Sagemaker).
- Experience with collaborative development tools (e.g., Git, JIRA) is a plus.
- Prior experience in financial services, banking, or credit risk modelling is beneficial.
- 5-8 years experience in a Data/Analytics role, ideally within a Financial Institution
Why This Role
- Opportunity to build an analytics function from the ground up in a cutting-edge, entrepreneurial environment.
- Work with massive datasets and modern tools at the forefront of fintech innovation.
- High-impact role directly contributing to investment and portfolio decision-making.