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About the Role

This role strengthens the data science capability by delivering models and insights to improve underwriting profitability and unlock automation. It is an end-to-end role, owning projects from problem framing through deployment and ongoing monitoring. The role requires translating underwriting requirements into data science solutions and building confidence with commercial underwriters.

Responsibilities

• Lead data science projects end to end, from problem framing and data preparation through development, deployment and ongoing monitoring in production.
• Work alongside the actuarial team to surface insights that drive performance (eg. Reserving).
• Apply data science techniques to automate manual processes across the business.
• Use generative AI to enrich insight and unlock new opportunities for the roadmap, deploying and maintaining these solutions through the same MLOps patterns applied to traditional models.
• Research, assess and integrate external data sources, working with data scientists and actuaries to establish their quality, value and fitness for use.
• Address the data quality issues that constrain modelling, including the matching of premium and claims for delegated business.
• Support the business with proactive analytics and insights, coordinating delivery with the Data Science and Data Analytics Manager.
• Design, build and maintain machine learning pipelines in a cloud environment, applying sound software engineering practice.
• Set and raise the team's standards for version control, testing, CI/CD, model versioning and reproducibility.
• Own deployed models in life, monitoring performance and drift and acting on degradation before it reaches the business.
• Ensure models are documented and explainable to a standard appropriate for a regulated environment.
• Work with technical and non technical stakeholders across the business to identify, document, analyse and prioritise requirements for data science products.
• Coordinate with IT and Data Engineering to shape the data foundations these products depend on, and contribute to best practice and operational procedures for data management.
• Produce clear deliverables and communicate findings and their limitations to audiences without a technical background.
• Coach data scientists and data analysts, through code review, pairing and technical mentoring, to build depth in modelling and production practice.
• Work with the Data Science Manager to upskill the team in new and emerging techniques, creating flexibility of resource while maintaining clear accountability.
• Contribute to the data science backlog and roadmap, advocating for projects with demonstrable value.

Requirements

• Well developed Python, written to production standard, with object-oriented design, testing and code review as normal practice.
• Demonstrable experience of personally taking models into production and supporting them in life.
• 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.
• Software engineering fundamentals: version control, branching strategy, code review, automated testing, dependency and environment management.
• MLOps and CI/CD in practice, covering pipeline orchestration, model versioning, automated deployment, monitoring and retraining. Platform agnostic; what matters is knowing the patterns and being able to discuss them in technical detail.
• 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.
• Data wrangling and pre-processing, including the ambiguous, incomplete and inconsistent data typical of insurance.
• Statistical foundations sufficient to design sound experiments, quantify uncertainty and challenge conclusions that the data does not support.
• Insurance experience, (specifically in Lloyds market) pricing or underwriting in a regulated environment, and comfort working alongside actuarial methodology.
• Practical experience deploying generative AI or LLM based solutions, including retrieval patterns, evaluation and cost, latency management and observability.
• Distributed processing frameworks such as PySpark.
• Data visualisation and reporting, for example Power BI.
• Commercial judgement: chooses problems by the value they create, and knows when a simpler solution is the right one.
• Communicates clearly with non-technical audiences, including the limitations and uncertainty in their work, not only the results.
• Manages competing priorities and stakeholders without losing delivery focus.
• Works well across teams, and builds capability in others rather than concentrating knowledge in themselves.
• Sound ethical judgement and awareness of data privacy, fairness and regulatory obligations and the commercial impact of deliverables.

Skills

• Python
• Object-oriented design
• Testing
• Code review
• Machine learning
• Scikit learn
• Pandas
• NumPy
• Statsmodels
• Version control
• Branching strategy
• Automated testing
• Dependency management
• Environment management
• MLOps
• CI/CD
• Pipeline orchestration
• Model versioning
• Automated deployment
• Monitoring
• Retraining
• Azure ML
• Azure DevOps
• AWS
• GCP
• SQL
• Relational data modelling
• Data wrangling
• Data pre-processing
• Statistical analysis
• Generative AI
• LLM
• Retrieval patterns
• PySpark
• Power BI

Work Type

• Full time
• Hybrid

About the Company

• AEGIS London is an equal opportunities employer and recognises the value of a diverse workforce in facilitating better decision making and business growth. We encourage a variety of differing views, perspectives and insights to create a collaborative working environment. Diversity and Inclusion are fundamental to our business and we encourage applications from all backgrounds recognising the diversity of society and our customers.
• As a business, we understand individual circumstances may differ and aim to be adaptable and to support flexible working practices. Talk to our recruitment team to understand how AEGIS London can help support you in reaching your full potential

Equal Opportunity

• AEGIS London is an equal opportunities employer and recognises the value of a diverse workforce in facilitating better decision making and business growth. We encourage a variety of differing views, perspectives and insights to create a collaborative working environment. Diversity and Inclusion are fundamental to our business and we encourage applications from all backgrounds recognising the diversity of society and our customers.
• It’s important to us that you are able to perform at your best when applying for a role with AEGIS London. If there are any adjustments we can reasonably make to ensure that the process is accessible for you please telephone us on +44(0)20 7856 7856 or email recruitment@aegislondon.co.uk