Senior Data Scientist

What you'll be doing

• Build and iterate on multimodal AI models that reduce claims cost and improve claims processing, including models that analyse emails, documents, and claim summaries for operational teams
• Develop machine learning models that support claims automation, including use cases such as negotiation strategies, litigation strategies, and total loss prediction
• Explore and evaluate new data sources that could improve model performance and decision-making, such as fraud signals, open banking, and telematics data
• Design and build agentic AI solutions to automate and streamline claims workflows
• Collaborate closely with Product, Data, and Engineering teams to test hypotheses, develop new features, and turn ideas into production-ready solutions
• Work with the MLOps team to improve data science and AI model infrastructure, including deployment, monitoring, evaluation, and feedback loops
• Help define the right technical approach for problems, balancing speed, quality, and scalability while ensuring solutions are practical for the business
• Set a strong standard for experimentation, measurement, and model performance, helping the team understand impact, uncertainty, and trade-offs clearly

Who You Are

• You think in systems: you can connect the dots between data science, engineering, and product to shape scalable solutions that build on each other over time.
• You're confident in challenging assumptions and pushing for the right approach, using strong communication skills to influence stakeholders across seniority levels and disciplines with clear, pragmatic reasoning.
• You thrive in ambiguity and change, staying resilient and effective during transitions while bringing structure, clarity, and momentum to complex problem spaces.
• You're motivated by real-world impact, partnering closely with cross-functional teams to drive meaningful automation and better customer outcomes across the claims journey.

What You'll Bring

• Strong commercial experience delivering end-to-end machine learning solutions, from problem framing and experimentation through to production deployment and ongoing monitoring
• Hands-on experience building and shipping production AI or machine learning systems, including evaluation, quality considerations, and integration into operational workflows
• Experience working on applied problems involving structured and unstructured data, with an interest in multimodal modelling and AI systems
• A strong statistical and modelling foundation, with experience working on risk-based decisioning or other complex, uncertain problem domains
• Proven ability to work cross-functionally with Product, Engineering, Operations, and MLOps to deliver scalable solutions
• Strong communication and stakeholder management skills, with confidence in discussing trade-offs and pushing back constructively when needed