London, United Kingdom | Posted on 26/08/2026
We are recruiting for a Senior Data Scientist on behalf of amarket leading Lloyd’s Syndicate.
The successful candidate will strengthen the business’s datascience capability by delivering models and insights that improve underwritingprofitability, automation and efficiency. It is a true end-to-end role, owningprojects from problem framing through to deployment and ongoing modelperformance, with strong object-oriented programming, machine learning andstatistical skills, and experience taking models into production, designing MLpipelines and working with MLOps, CI/CD and Azure.
Key Responsibilities
Delivery of data science products
- Leading data scienceprojects end-to-end, from problem framing through development, deploymentand ongoing monitoring in production.
- Working alongside theactuarial team and coordinating with the Data Science and Data AnalyticsManager to support the business with proactive analytics and insights.
- Using generative AI toenrich insight and unlock new opportunities, deployed and maintainedthrough the same MLOps patterns applied to traditional models.
Engineering and MLOps standards
- Designing, building andmaintaining machine learning pipelines in a cloud environment, applyingsound software engineering practice.
- Owning deployed modelsin life, monitoring performance and drift, and ensuring models aredocumented and explainable to a standard appropriate for a regulatedenvironment.
Stakeholder engagement and requirements
- Working with technicaland non-technical stakeholders to identify, document, analyse andprioritise requirements for data science products.
- Producing cleardeliverables and communicating findings and their limitations to audienceswithout a technical background.
Team and capability building
- Coaching and upskillingdata scientists and data analysts through code review, pairing andmentoring, and contributing to the data science backlog and roadmap.
Key Requirements
- Strong Python, writtento production standard, with object-oriented design and softwareengineering fundamentals: version control, code review, automated testing,dependency and environment management.
- Machine learning acrossthe standard toolkit (scikit learn, pandas, NumPy, statsmodels orequivalents), with sound judgement about model selection, validation andthe limits of what the data supports.
- MLOps and CI/CD inpractice, covering pipeline orchestration, model versioning, automateddeployment, monitoring and retraining.
- Cloud based machinelearning delivery, ideally on Azure (Azure ML, Azure DevOps), withequivalent AWS or GCP experience considered.
- SQL and relational datamodelling, with the ability to work efficiently against large datasets.
- Statistical foundationssufficient to design sound experiments, quantify uncertainty and challengeconclusions that the data does not support.
- Insurance experience(specifically in the Lloyd’s market), pricing or underwriting in aregulated environment.
- Practical experiencedeploying generative AI or LLM based solutions.
Altogether, this role suits a hands-on data scientist whocan own the full lifecycle from problem framing to production and translatethat work into commercial value alongside underwriters and actuaries.
Wedonotretainanypersonalinformationwithoutyourpermission.Pleaserefertoourdataprotectionpolicyonourofficialwebsiteformoreinformation.
#J-18808-Ljbffr…