Lead, Data Scientist
Financial Services, Banking
Minimum Requirements
- Post Graduate Degree Information Technology
- 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.
- 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.
- 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.
- 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.
- 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.
- Exposure to AI governance, model risk management, responsible AI, monitoring, explainability, and production model lifecycle management (MLOps/LLMOps).
- 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.
- leading or mentoring engineers and data scientists, while providing technical leadership and execution oversight across complex AI programmes.
- 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
- Data Analysis
- Database Administration
- Data Integrity
- Knowledge Classification
- Research & Information Gathering
Responsibilities
- 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.
