Analytics Solutions Lead

Overview

Senior engineering leader responsible for turning pricing, portfolio and underwriting models into robust, production-grade capabilities embedded in core systems. You will set standards and architecture for scalable, auditable analytics across a Commercial Insurance portfolio in EMEA. As the most senior technical practitioner in the analytics and AI team, you’ll raise engineering maturity while delivering deployment-ready solutions, not research. You will work closely with data scientists, engineers, actuaries and underwriters to translate analytics into production impact.

Pay / Benefits

• competitive salary
• pension scheme
• discretionary bonus
• hybrid working
• Private Medical cover
• Employee Share Purchase Plan

Responsibilities

• Design scalable deployment patterns for ML models (batch and API scoring)
• Define model lifecycle standards: versioning, retraining triggers, monitoring, documentation
• Embed pricing, conversion and risk models into underwriting workflows and core platforms
• Establish CI/CD standards for analytics delivery pipelines
• Ensure reproducibility and robustness of deployed solutions
• Implement monitoring frameworks for model performance, drift, and portfolio impact
• Develop dashboards for pricing and propensity models
• Collaborate with actuarial and risk teams on governance, audit readiness and documentation
• Ensure compliance within regulated insurance environments
• Provide hands-on technical leadership to data scientists and data engineers
• Conduct code reviews, pair programming and architectural oversight
• Standardise development practices, tooling and ways of working
• Act as the technical authority on model build, testing and deployment
• Operationalise AI/Workflow integration including document intelligence and workflow augmentation
• Define scalable deployment patterns for emerging AI initiatives (GenAI)

Key requirements

• Proven experience in data science, ML engineering, or analytics engineering with production systems trajectory
• Experience deploying ML models into production (batch and/or real-time scoring) in commercial environments
• Experience integrating analytics into operational workflows (embedded decision support)
• Experience designing and operating model monitoring frameworks (drift, performance, alerts)
• Strong Python ecosystem expertise (production-quality code)
• Experience with ML lifecycle tooling (MLflow, Azure ML, SageMaker or equivalent)
• Cloud platform experience — Azure preferred
• Experience in regulated industries — insurance or financial services preferred
• leadership and mentoring
• communication and collaboration
• code review and architectural oversight
• Python production-quality development
• ML lifecycle tooling: MLflow, Azure ML, SageMaker
• Cloud: Azure

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