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  "job_description": "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.\n\nThis 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.\n\nYou will work in a neo-bank/fintech-style environment, using cutting-edge technology to analyze terabytes of data and support high-impact investment decisions.\n\nResponsibilities:\n• Develop, implement, and validate statistical and machine learning models for analysing non-performing loan portfolios.\n• Collaborate with cross-functional teams—including credit, collections, and data engineering—to translate business objectives into robust analytical solutions.\n• Build software using modern technology to enable investing and asset management at scale.\n• Apply Bayesian modelling and probabilistic programming techniques to address uncertainty and improve prediction accuracy.\n• Analyse large-scale datasets to identify key drivers, trends, and early warning signals within NPL portfolios.\n• Clearly communicate model results, insights, and recommendations to stakeholders, including both technical and non-technical audiences.\n• Stay current with advances in statistical modelling, machine learning, and data science, continuously evaluating and integrating new techniques and tools.\n\nRequirements:\n• University degree in a STEM field (e.g., Mathematics, Statistics, Computer Science, Engineering, Physics, Economics); advanced degree preferred.\n• Strong expertise in statistical modelling, Bayesian inference, and machine learning.\n• Proficient in Python (using libraries such as NumPy, pandas, scikit-learn, PyMC or Stan)\n• Experienced in SQL. Ability to write efficient and robust queries.\n• Demonstrated experience working with large and complex datasets.\n• Ability to communicate complex analytical concepts clearly and effectively to a range of audiences.\n• Experience with model governance, documentation, and deployment best practices.\n• Experience with cloud environments (e.g., AWS Sagemaker).\n• Experience with collaborative development tools (e.g., Git, JIRA) is a plus.\n• Prior experience in financial services, banking, or credit risk modelling is beneficial.\n• 5-8 years experience in a Data/Analytics role, ideally within a Financial Institution\n\nWhy This Role\n• Opportunity to build an analytics function from the ground up in a cutting-edge, entrepreneurial environment.\n• Work with massive datasets and modern tools at the forefront of fintech innovation.\n• High-impact role directly contributing to investment and portfolio decision-making.",
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  "job_posted_at_datetime_utc": "2026-03-10T00:00:00.000Z"
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