Data Scientist, Underwriting – AI Engineer

Canopius Group

. Work with underwriters, portfolio managers and product heads to understand challenges and define worthwhile problems and success criteria .

Posted 11/6/2026full-timeLondon • United KingdomMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in applied statistics and machine learning to solve business problems, with proficiency in Python and SQL for data analysis and automation. Capable of transforming unstructured data into structured formats and effectively communicating findings to both technical and non-technical stakeholders.

Highest-signal resume keywords

Applied StatisticsMachine LearningPython ProgrammingSQL SkillsLarge Language Models

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Statistical AnalysisMachine Learning ModelsData AutomationPrototype DevelopmentData Structuring

Soft Skills

CommunicationCollaborationProblem-Solving

Tools & Technologies

Version ControlDocument AIAI WorkflowsData Science Tools

Industry Keywords

Underwriting AnalyticsInsuranceFinancial RiskLloyd's MarketPortfolio Management

Tech Stack

Tools & technologies

PythonSQL

About the role

Key responsibilities & impact

Work with underwriters, portfolio managers and product heads to understand challenges and define worthwhile problems and success criteria
Select and develop appropriate solutions, including analysis, automation, AI workflows, statistical or machine learning models, and third-party products
Apply large language models, document AI and automation to convert submissions, emails and documents into structured data and reduce repetitive underwriting work
Build working prototypes with underwriters and incorporate their feedback
Hand successful prototypes to central data and technology teams with clear requirements, results and success measures, and support delivery and adoption
Build statistical and machine learning models such as segmentation, triage and propensity models to support underwriting decisions
Analyse broker flow, quote-to-bind conversion and portfolio performance
Track changes in rates, terms and conditions to identify market shifts
Present findings in plain language to underwriting teams and senior management
Help shape the new Underwriting Analytics function and keep current with data science and AI developments

Requirements

What you’ll need

Applied statistics and machine learning methods to real business problems
Ability to design analyses and experiments that measure impact
Clean, well-structured Python and SQL skills
Experience using version control and testing
Comfortable working with large, imperfect datasets
Built applications or prototypes using large language models, including prompt design, structured outputs and retrieval
Ability to evaluate large language model performance
Experience turning unstructured information such as documents, spreadsheets or emails into structured data and checking accuracy
Experience taking a business problem from initial discussion to a tested or used working prototype and measuring its impact
Experience handing over models or prototypes to engineering or technology teams and supporting delivery
Ability to explain models or tools, performance and limitations to non-technical colleagues
Desirable: data science experience in pricing, claims, exposure management or portfolio management in insurance or another financial-risk setting
Desirable: knowledge of specialty or Lloyd's market insurance
Desirable: training or fine-tuning models for text and document tasks
Desirable: product ownership or experience working in agile teams alongside engineers and business users

Benefits

Comp & perks

Permanent full-time employment
Hybrid working arrangement