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  "job_description": "Python Developer / Data Scientist - Wealth Management\n\nLocation: London\n\n£60000 - £75000 + bonus + benefits\n\nThis role will not provide sponsorship\n\nAre you passionate about using your Python and machine learning skills to drive smarter client engagement? We’re looking for a technically strong and commercially minded Python Developer / Data Scientist to help design, refine, and scale intelligent data-driven solutions within our CRM environment.\n\nYou’ll play a key role in developing the Next Best Action engine — a platform that generates intelligent prompts to help prioritise client engagement. Working alongside data scientists, analysts, and engineers, you’ll enhance algorithms, optimise code performance, and create tools that bring measurable business value.\n\nWhat you’ll do\n• Implement and enhance algorithmic business rules and machine learning models.\n• Refine data processing logic and improve the scalability of Python codebases.\n• Develop analytical tools and visual dashboards that inform decision-making.\n• Collaborate closely with stakeholders to understand use cases and deliver data solutions.\n• Maintain high standards of code quality, version control, and documentation.\n• Support the ongoing identification and development of new data science applications.\n\nWhat you’ll bring\n• Degree in Computer Science, Data Science, Mathematics, Engineering, or similar.\n• Strong experience with Python (Pandas, NumPy, scikit-learn) and SQL.\n• Knowledge of machine learning techniques, statistics, and probability.\n• Experience using Git for version control.\n• Familiarity with DBT and Power BI (or other data visualisation tools) is a plus.\n• Strong attention to detail, logical mindset, and problem-solving ability.\n• Excellent communication skills and a proactive, collaborative attitude.\n\nWhy join\n\nYou’ll be part of a highly analytical and forward-thinking team that’s shaping how data drives client relationships and business growth. This role is ideal for someone who enjoys building production-quality data tools, collaborating across teams, and continuously learning as part of an evolving data journey.",
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