Senior Data Scientist

We are recruiting for aSenior Data Scientiston behalf of a market leading Lloyd's Syndicate.

The successful candidate will strengthen the business's data science capability by delivering models and insights that improve underwriting profitability, automation and efficiency. It is a true end-to-end role, owning projects from problem framing through to deployment and ongoing model performance, with strong object-oriented programming, machine learning and statistical skills, and experience taking models into production, designing ML pipelines and working with MLOps, CI/CD and Azure.

Key Responsibilities

Delivery of data science products

• Leading data science projects end-to-end, from problem framing through development, deployment and ongoing monitoring in production.
• Working alongside the actuarial team and coordinating with the Data Science and Data Analytics Manager to support the business with proactive analytics and insights.
• Using generative AI to enrich insight and unlock new opportunities, deployed and maintained through the same MLOps patterns applied to traditional models.

Engineering and MLOps standards

• Designing, building and maintaining machine learning pipelines in a cloud environment, applying sound software engineering practice.
• Owning deployed models in life, monitoring performance and drift, and ensuring models are documented and explainable to a standard appropriate for a regulated environment.

Stakeholder engagement and requirements

• Working with technical and non-technical stakeholders to identify, document, analyse and prioritise requirements for data science products.
• Producing clear deliverables and communicating findings and their limitations to audiences without a technical background.

Team and capability building

• Coaching and upskilling data scientists and data analysts through code review, pairing and mentoring, and contributing to the data science backlog and roadmap.

Key Requirements

Essential

• Strong Python, written to production standard, with object-oriented design and software engineering fundamentals: version control, code review, automated testing, dependency and environment management.
• Machine learning across the standard toolkit (scikit learn, pandas, NumPy, statsmodels or equivalents), with sound judgement about model selection, validation and the limits of what the data supports.
• MLOps and CI/CD in practice, covering pipeline orchestration, model versioning, automated deployment, monitoring and retraining.
• Cloud based machine learning delivery, ideally on Azure (Azure ML, Azure DevOps), with equivalent AWS or GCP experience considered.
• SQL and relational data modelling, with the ability to work efficiently against large datasets.
• Statistical foundations sufficient to design sound experiments, quantify uncertainty and challenge conclusions that the data does not support.

Desirable

• Insurance experience (specifically in the Lloyd's market), pricing or underwriting in a regulated environment.
• Practical experience deploying generative AI or LLM based solutions.

Altogether, this role suits a hands-on data scientist who can own the full lifecycle from problem framing to production and translate that work into commercial value alongside underwriters and actuaries.

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