Senior Data Scientist

We are recruiting for a Senior Data Scientist on 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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