We're looking for a Senior Data Scientist to join a high-performing team working on advanced analytics and machine learning solutions within the insurance and risk space.
This is a hands-on, product-focused role where you'll design, build, and deploy scalable data science solutions that directly support decision-making across pricing, underwriting, fraud detection, and claims.
You'll collaborate closely with cross-functional teams including engineering, product, and business stakeholders, helping to translate complex data into impactful, real-world applications.
What You'll Be Doing
- Design and develop end-to-end data science solutions, from data processing (ETL) through to model deployment
- Explore and analyse large, complex datasets to identify insights and use cases
- Build and optimise machine learning models for real-world applications
- Work closely with data engineering and technology teams to deliver scalable, production-ready solutions
- Communicate findings and model outputs clearly to both technical and non-technical stakeholders
- Contribute to project planning, timelines, and delivery
- Support development of data and analytics infrastructure, including cloud migration and best practices
- Mentor junior team members and promote coding and data science best practices
What We're Looking For
Essential Skills & Experience
- 5+ years' experience in data science, machine learning, or advanced analytics
- Strong programming skills in Python or R
- Advanced SQL and data manipulation skills
- Experience building and maintaining data pipelines
- Strong understanding of data quality, validation, and testing
- Experience working with large-scale datasets and distributed computing (e.g. Spark)
- Proven ability to write clean, maintainable, production-level code
- Strong communication skills with the ability to explain complex concepts clearly
- Experience working in cross-functional teams
Desirable Experience
- Experience within insurance, financial services, or risk modelling
- Knowledge of pricing, underwriting, or fraud analytics
- Experience with Azure (ML, Data Lake, Blob Storage) or similar cloud platforms
- Familiarity with Databricks
- Experience with BI/visualisation tools (e.g. Power BI, R Shiny)
- Experience working in Linux environments
