The role holder shall also be expected to guide and lead team members from a functional perspective and help them build up a strong business understanding.

Governance and Committee Memberships :

- Facilitate and coordinate all governance forums at department - site levels.

In this role, you will :

Principal Accountabilities and Responsibilities :

- Lead the global strategy and operating model for AI-driven analytics across operations, defining value, risk, and performance KPIs aligned with enterprise priorities.

- Shape and govern multi-agent architectures (e.g., LangGraph), including Supervisor Agent frameworks to manage execution, prioritisation, and safe autonomy at scale.

- Build a reusable capability stack for document intelligence, validation, and decision reasoning, ensuring structured outputs, schema validation, and full traceability across functions such as collections and underwriting.

- Establish assurance-by-design, incorporating reflection, LLM-vs-LLM validation, safety controls, and quality standards to deliver reliable, repeatable outcomes across regions.

- Embed policy intelligence and operational memory components to drive consistency, reduce operational risk, and strengthen resilience throughout the analytics lifecycle.

- Drive production adoption in collaboration with platform, orchestration, and DevOps teams, ensuring explainability, auditability, and compliance with regulatory, data security, and internal control requirements.

To be successful, you will :

- Bring at least 9 years experience in Analytics or Data Science within financial services, demonstrating a strong track record of leading large-scale AI, advanced analytics, and data transformation programmes.

- Exhibit deep expertise in shaping and executing advanced Data Science and Generative AI strategies, including the deployment of machine learning models (deep learning, NLP, reinforcement learning) and the enterprise integration of LLM-based solutions. You will be highly skilled in prompt engineering, including Chain-of-Thought and advanced prompting frameworks.

- Demonstrate hands-on experience in designing, institutionalizing, and scaling robust experimentation frameworks (such as A/B testing) to validate models, quantify business impact, and enable data-driven decision-making across the organisation.

- Possess advanced technical proficiency in machine learning methodologies - spanning classification, clustering, ensemble models, and neural networks - with practical expertise in Python and SQL. You will also be well-versed in model governance, responsible AI principles, and regulatory compliance.

- Have a comprehensive understanding of banking operations, regulatory expectations, and risk management frameworks, with a proven ability to lead high-performing teams and influence senior stakeholders in complex, matrixed environments.

- Display exceptional analytical, problem-solving, and executive communication skills, with the ability to translate complex AI and analytics insights into actionable business strategies.

Functional Knowledge and Leadership :

- Demonstrate strategic vision by defining, communicating, and executing innovative analytical strategies that support operational and business objectives.

- Lead the end-to-end lifecycle of advanced analytical models, from development and deployment to validation and optimisation, ensuring robust data governance and high data quality.

- Possess strong technology acumen, with experience in cloud platforms (GCP, AWS, Azure), big data technologies (Spark, Hadoop), and modern analytical tools (Python, R, SQL, SAS). You will guide technical architecture and tool selection for agentic AI initiatives.

- Bring hands-on experience with LLMs, agentic architectures (e.g., LangChain, LangGraph), retrieval-augmented generation, vector databases, and knowledge retrieval patterns.

- Have practical exposure to cloud-native engineering, microservices, DevOps tooling, and container platforms (Docker/Kubernetes), ideally within regulated AI environments.

- Hold a postgraduate qualification in a quantitative discipline or equivalent experience in Data Science, ML, NLP, or deep learning.