Lead, Data Scientist

• Lead the technical execution and engineering delivery of AI and GenAI solutions across Group Risk ensuring scalable secure and production-ready implementations.
• Translate business problems and strategic objectives into clear technical requirements solution architectures and measurable AI use cases ensuring alignment between stakeholder needs and engineering delivery.
• Partner closely with business stakeholders risk teams and product owners to shape and prioritise high-value AI opportunities conducting rapid prototyping and proof-of-value exercises to assess feasibility and impact.
• Design solution architectures and technical patterns for AI use cases producing high-quality solution designs technical documentation and architecture artefacts.
• Drive the implementation and optimisation of Virtual Risk Manager / AI assistant capabilities including LLM adoption orchestration retrieval and performance improvements.
• Build and automate ML/LLM pipelines enabling rapid experimentation evaluation monitoring and deployment through robust engineering practices.
• Present solution designs and technical approaches at architecture forums governance committees and senior stakeholder engagements.
• Research and apply emerging AI techniques and technologies to improve efficiency insight generation automation and decision-making across Group Risk.

Qualifications :

Minimum Qualifications

•  Post Graduate Degree Information Technology

Technical Skills & Experience

• Strong hands-on experience designing building and deploying AI/ML and GenAI solutions on Microsoft Azure including Azure OpenAI Azure AI Foundry Azure AI Services Azure Machine Learning Azure Kubernetes Service (AKS) Azure Container Apps APIs and cloud-native architectures.
• Deep understanding of machine learning large language models (LLMs) Retrieval-Augmented Generation (RAG) prompt engineering model evaluation fine-tuning approaches agentic AI systems multi-agent orchestration and conversational AI architectures.
• Strong software engineering discipline including Python development API development source control (Git) CI/CD pipelines automated testing containerisation DevOps practices reusable code patterns and secure production-grade engineering standards.
• Experience with major AI/ML frameworks and tooling such as PyTorch TensorFlow scikit-learn LangChain LlamaIndex Semantic Kernel vector databases model orchestration frameworks and observability/evaluation tooling for AI systems.
• Experience building end-to-end AI products and intelligent applications including integration of AI models into enterprise systems through APIs batch streaming and event-driven architectures ensuring scalability reliability and maintainability.
• Strong experience working with structured and unstructured data including feature engineering embeddings knowledge retrieval document processing semantic search experimentation and rapid prototyping.
• Experience developing business-facing AI applications and interfaces using Python frameworks and modern web technologies to enable intuitive interaction with AI capabilities.
• Familiarity with data visualisation and insight tools (e.g. Power BI) to support business consumption explainability and interpretation of AI-driven outputs.
• Experience implementing MLOps and LLMOps practices including model lifecycle management experimentation monitoring prompt/version management evaluation observability and production support.
• Understanding of responsible AI model governance explainability bias monitoring security and risk controls required for enterprise AI deployments in regulated environments.

Preferred Experience

• Exposure to AI governance model risk management responsible AI monitoring explainability and production model lifecycle management (MLOps/LLMOps).
• Experience leading or mentoring engineers and data scientists while driving execution in a fast-paced delivery environment.
• Proven ability to engage and influence senior stakeholders including executive leadership (e.g. CROs Risk Executives CIOs senior governance forums) translating complex technical concepts into clear business language and influencing decision-making.
• Strong executive communication and stakeholder management capability with experience presenting at senior committees architecture forums governance bodies and business leadership engagements.
• Experience leading or mentoring engineers and data scientists while providing technical leadership and execution oversight across complex AI programmes.

Additional Information :

Behavioural Competencies:

• Adopting Practical Approaches
• Articulating Information
• Challenging Ideas
• Checking Things
• Examining Information
• Exploring Possibilities
• Interacting with People
• Interpreting Data
• Meeting Timescales
• Producing Output
• Providing Insights
• Team Working

Technical Competencies:

• Data Analysis
• Database Administration
• Data Integrity
• Knowledge Classification
• Research & Information Gathering

Remote Work :

No

Employment Type :

Full-time