Senior Associate - AI

As a Senior Associate - AI at Davies, you will design, build, and deploy AI and machine learning solutions that deliver measurable business value across the claims lifecycle and broader professional services operations. You will work across the full AI lifecycle, from data acquisition and feature engineering through to model development, evaluation, deployment, and operational monitoring. Working closely with stakeholders across the Group, you will translate business challenges into scalable, production-grade AI solutions. Assemble, cleanse, and model complex datasets from across the Group to meet functional and non-functional business requirements, using Azure tool chain and any other relevant tools. Design, build, evaluate, and deploy machine learning and AI solutions for business-focused use cases including claims triage, fraud detection, outcome prediction, document processing, conversational AI, and operational efficiency optimisation. Collect and structure data through a combination of business analysis, study design, and automated collection pipelines — identifying the most valuable signals within complex datasets. Build and maintain analytical tools and reporting solutions that provide stakeholders with actionable insight into key business performance metrics, including operational efficiency, claims outcomes, and customer experience. Create clear, compelling reports, dashboards, and presentations that translate complex data findings into simple, actionable recommendations for business, product, and executive audiences. Develop and implement robust evaluation frameworks for machine learning and generative AI systems, including offline testing, model validation, performance monitoring, and continuous improvement in production environments. Partner with data engineers, software engineers, AI engineers, and platform teams to deliver secure, scalable, and maintainable production AI solutions. Contribute to the continuous improvement of data science practice at Davies, including tooling, methodology, model governance, and knowledge sharing across the AI Office and wider Group. Design and implement AI-enabled services and microservices using modern software engineering practices, APIs, cloud-native architectures, and containerised deployment patterns. Education A degree in computer science, applied mathematics, statistics, data science, or a related quantitative field; or equivalent demonstrable experience in a data science or applied science role. Knowledge, Skill and Ability Expert-level Python development skills for AI applications, machine learning, data processing, API development, and automation, with strong command of libraries such as pandas, scikit-learn, PyTorch, Pydantic, and related ecosystems. Experience with MLOps and LLMOps practices including CI/CD pipelines, model versioning, experiment tracking, automated evaluation, deployment automation, observability, and governance. Hands-on experience designing, building, and evaluating LLM-based applications and generative AI systems, including prompt engineering, RAG architectures, embedding models, vector databases, structured outputs, function/tool calling, agentic workflows, context management, evaluation frameworks, and safety/guardrail implementation. Experience with frameworks and platforms such as Azure OpenAI, OpenAI APIs, LangChain, LangGraph, Semantic Kernel, or equivalent. Strong software engineering skills including API design and implementation using FastAPI, asynchronous programming, testing, dependency management, versioning, observability, authentication, and integration of AI services into production systems. Experience delivering solutions using Microsoft Azure services including Azure OpenAI, Azure AI Foundry, Azure Data Factory, Microsoft Fabric, Azure Functions, Azure Storage, and related cloud-native services. Experience designing and evaluating machine learning models — including classification, regression, clustering, and NLP techniques — and deploying them in production or near-production environments. SQL skills and experience working with relational databases, data warehouses, and medallion architecture data lake patterns. Soft Skills Excellent written and verbal communication skills, demonstrated through the preparation of reports, presentations, and technical documentation for diverse audiences including senior leadership. Analytical and problem-solving mindset with exceptional attention to detail — comfortable searching through large, messy datasets to surface usable and statistically meaningful insight. Ability to work independently and collaboratively across teams with different technical backgrounds, translating between data science concepts and business requirements with clarity. Experience applying data science in insurance, financial services, claims management, or other regulated industries is a strong advantage, particularly where data governance and model explain ability are key requirements. Essential (Non- Negotiable) Very Strong Python (for all the requirements below) Knowledge of NLP techniques and how to do development with LLM Experience designing and deploying microservices and AI services using FastAPI, Docker, REST APIs, dependency management, testing frameworks, and cloud-native deployment patterns. Understand how to work with data - cleansing, insights collection, iterative development, storage options Experience taking AI systems from proof-of-concept to production.