We are seeking a creative and technically adaptable candidate to design, develop, and automate next-generation credit and customer toolkits. This role focuses on building scalable data and modelling pipelines that enable real-time deployment and high-impact decision-making. Leveraging strong quantitative computing and machine learning skills, the incumbent will drive innovation and enhance predictive performance across key portfolios. Crucially, this role champions the use of Agentic AI with a "human-in-the-loop" approach to accelerate productivity and redefine the model lifecycle. Job Responsibilities Advanced Data Engineering & Pipeline Architecture Develop & Optimise : Build robust analytics data pipelines using Python, Spark, Airflow, and distributed computing frameworks (containerised). Automate & Scale : Design reusable analytical components with capabilities for automated feature engineering and feature store population to ensure solution consistency and scalability. MLOps & Model Lifecycle Management Operational Excellence : Implement best‑practice MLOps methodologies, including CI/CD, containerisation, and cloud‑ready model operations. End-to-End Tracking : Orchestrate modelling pipelines using tools like MLFlow for comprehensive tracking, versioning, and deployment. Real-Time Architecture : Design and deploy real-time model architectures with embedded automated monitoring capabilities to ensure reliability. Driving Business Value & Strategy Signal from Noise : Distil high‑value data elements from Big Data to build and operationalise decision‑grade insights, analytics and tools. Solution Delivery : Translate business requirements into clear hypotheses and use cases with defined success criteria and value metrics. Performance Monitoring : Track the value of deployed solutions, reporting on ROI while proactively identifying anomalies or areas for improvement. Hypothesis-Driven Experimentation : Research, prototype, and introduce new technologies, such as enhanced optimisation techniques, to drive profitability and efficiency. Tech Radar : Actively monitor emerging technologies, open‑source projects, and academic research to keep the stack cutting‑edge. Stakeholder Engagement & Culture Cross-Functional Partnership : Build strong relationships with business, operations, product, and risk partners to influence decision‑making and manage expectations throughout the development cycle. Communication : Articulate complex technical findings clearly to both technical and non‑technical audiences. Mentorship & Growth : Foster a culture of excellence by coaching junior analysts, conducting code reviews, and sharing knowledge on industry trends. Professional Exposure The ideal candidate will have practical, hands‑on exposure to: Software Engineering / Coding Fundamentals: Solid grounding in computer science/coding principles, including Object‑Oriented Programming (OOP), design patterns, data structures, and algorithmic complexity (Big-O). Distributed Computing & Big Data: Working with large‑scale data processing systems and distributed environments. Modern DevOps Integration: Active usage of CI/CD pipelines, version control (Git), and containerisation technologies (Docker/Kubernetes) within a microservices or API‑driven architecture. Deep Learning & Optimisation: Proficiency with ML frameworks (e.g., TensorFlow, PyTorch, Scikit‑learn) and application of continuous/discrete mathematical optimisation techniques. Model Governance: Productionising models with rigorous tracking, specific versioning, and governance using tools such as MLFlow. Innovative & Curious: A relentless learner who stays ahead of the curve, passionate about applying emerging technologies and modern analytical approaches to solve old problems. Analytical Problem Solver: Possesses the intellect to deconstruct complex, ambiguous modelling challenges into scalable, logical solutions. Collaborative Powerhouse: A cross‑functional partner who drives impact through strong stakeholder management, capable of delivering results individually or by influencing others. Resilient & Adaptable: Thrives in rapidly evolving environments; comfortable with ambiguity and quick to pivot strategies when business needs change. Technical Communicator: Translates dense technical concepts into clear, actionable insights for non‑technical leadership. Owner's Mindset: Takes full accountability for the end‑to‑end delivery and reliability of modelling solutions. Force Multiplier: Demonstrates a coaching mindset, actively mentoring junior analysts to uplift the team's overall technical capability. Qualification Honours Degree in a quantitative or technical discipline, like Computer Science, Engineering (Industrial, Electrical, Computer), Mathematics/Applied Mathematics, Statistics, or Computational/Theoretical Physics. Preferred Master’s Degree (or higher) in a related quantitative field Minimum Experience Level 5-8 years of core experience in quantitative modelling, data science, or advanced analytics. Production Engineering: Demonstrated ability to write robust, modular, and well‑structured Python code for production environments. Domain Expertise: Proven track record in building and deploying machine learning models, with specific experience in Credit Risk or financial modelling being highly advantageous. Agile Delivery: Experience working within Agile data science or engineering squads. Technical / Professional Knowledge Industry trends Microsoft Office Principles of project management Relevant regulatory knowledge Relevant software and systems knowledge Risk management process and frameworks Business writing skills Microsoft Excel Business Acumen Quantitative Skills Coaching Communication Decision Making Quality Orientation Technical/Professional Knowledge and Skills #J-18808-Ljbffr