About this role
As a Senior Associate - AI / Data at Davies, you will collate, model, and interpret complex data to deliver business-focused insights and applied science solutions across the claims lifecycle and broader professional services operations. You will work across the full data science pipeline — from data assembly and feature engineering through to model development, validation, and deployment — partnering with stakeholders across the Group to turn data into measurable business value. Assemble, cleanse, and model large, complex datasets from across the Group to meet functional and non-functional business requirements, using the medallion architecture on the Davies Microsoft data platform (Azure Data Factory, Microsoft Fabric, and Power BI). Design, build, and validate statistical and machine learning models for business-focused use cases including claims triage, fraud detection, outcome prediction, 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 large, 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 experimental frameworks for data collection and model evaluation, including A/B testing, model validation, and performance monitoring in production environments. Partner with data engineers, AI engineers, and platform teams to ensure data science outputs are production-ready, well-documented, and integrated effectively into Davies systems and workflows. Work with stakeholders across data, product, design, and executive teams to understand their data science needs, define problem statements clearly, and deliver solutions that are technically rigorous and practically useful. 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.