About this role
- 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.