Analytics Solutions Lead

Chubb; Greater London; On-site; GBP 120,000 - 180,000; Full time

Job summary

Chubb is seeking a senior Analytics Solution Lead to operationalize analytics and AI within its EMEA insurance business. This deployment-first role sits at the intersection of data science, engineering and business—ensuring production-grade analytics drive measurable commercial outcomes.

You will own the full journey from model development to business adoption, collaborating with underwriters, pricing and portfolio managers, and leading adoption, deployment, and enablement across complex,

Responsibilities

• Embed directly with business stakeholders — underwriters, pricing leads, portfolio managers — to understand how analytics can change how they make decisions.
• Deploy solutions rapidly and iteratively: ship working capability quickly, gather real-world feedback, and improve — field-first deployment philosophy.
• Drive adoption of deployed analytics: ensure tools and models are being used, understood, and trusted by the people they were built for.
• Define solution specifications and deployment requirements that enable Solution Architects and Deployment Engineers to build and release production-grade analytics solutions.
• Collaborate with Solution Architects to ensure analytics designs conform to enterprise architecture standards, platform constraints, and non-functional requirements (scalability, security, latency, auditability).
• Define measurable success criteria for every deployed solution — not just technical metrics, but commercial outcomes (e.g. pricing accuracy, hit rate improvement, loss ratio movement, underwriter decision quality).
• Design and lead change management plans for analytics deployment — ensuring that new tools and models are adopted, not just installed.
• Develop and deliver targeted enablement for business users: from underwriter training on model outputs to pricing team workflows that embed decision support

Job description

The Role

This is a senior, forward-deployed analytics leadership role — responsible for taking analytics and AI capabilities out of development and into the commercial reality of Chubb's EMEA insurance business. This role is not a central analytics function. It is a deployment-first role, embedded at the intersection of data science, engineering, and business — where the measure of success is analytics operating reliably in production, driving measurable commercial outcomes.

The Analytics Solution Lead owns the full journey from analytical development to business adoption: designing production-ready solutions, driving deployment into operational systems, and embedding analytics into how underwriters, pricing teams, and portfolio managers actually make decisions. You will bring field learnings back to sharpen the analytics roadmap, proactively identify new deployment opportunities across the business, and lead the cultural shift that turns analytical output into operational capability.

It is a delivery and adoption role — combining the technical credibility to work alongside AI engineers, data scientists and architects, the business fluency to engage senior underwriting, pricing and Ops leaders, and the deployment mindset to get things done in complex, regulated environments.

Core Responsibilities

• Embed directly with business stakeholders — underwriters, pricing leads, portfolio managers — to understand how analytics can change how they make decisions, not just what information they have


• Deploy solutions rapidly and iteratively: ship working capability quickly, gather real-world feedback, and improve — consistent with a field-first deployment philosophy


• Drive adoption of deployed analytics: ensure tools and models are being used, understood, and trusted by the people they were built for


• Define solution specifications and deployment requirements that enable Solution Architects and Deployment Engineers to build and release production-grade analytics solutions


• Collaborate with Solution Architects to ensure analytics designs conform to enterprise architecture standards, platform constraints, and non-functional requirements (scalability, security, latency, auditability)


• Define measurable success criteria for every deployed solution — not just technical metrics, but commercial outcomes (e.g. pricing accuracy, hit rate improvement, loss ratio movement, underwriter decision quality)


• Design and lead change management plans for analytics deployment — ensuring that new tools and models are adopted, not just installed


• Develop and deliver targeted enablement for business users: from underwriter training on model outputs to pricing team workflows that embed decision support