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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
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Qualifications
Required Experience
- 8+ years in data science, analytics, analytics engineering, or forward-deployed analytics/AI roles — with a demonstrable track record of getting analytics into production in complex organisations
- Proven experience deploying analytics solutions end-to-end — from problem framing through to production, adoption, and impact measurement
- Experience embedding directly with business stakeholders and driving behavioral change through analytics — not just delivering technical outputs
- Demonstrated ability to measure and communicate the commercial impact of analytics deployments
- Experience working across analytics and engineering teams — translating model outputs and analytical designs into deployment-ready specifications
- Ability to engage credibly with Solution Architects on design trade-offs, platform constraints, and integration patterns
- Experience designing and operating model monitoring frameworks — drift detection, performance tracking, alerting
- Experience defining solution governance frameworks — handoff documentation, acceptance criteria, deployment standards
- Exposure to GenAI / LLM deployment in enterprise settings
- Cloud platform experience — Azure preferred
- Experience working in regulated industries — insurance or financial services strongly preferred
- Demonstrable experience with responsible AI practices — explainability, bias review, model auditability
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Desirable
- Experience with insurance platforms (Duck Creek, Guidewire, Acturis)
- Experience with pricing or actuarial model deployment
- Familiarity with MLOps concepts (CI/CD pipelines, model registries, automated testing) — awareness required, not hands-on ownership
- Experience with ML lifecycle tooling — MLflow, Azure ML, or equivalent
- Python proficiency — able to read and review analytical code, contribute where needed
- Consulting or forward-deployed engineering background (Palantir, QuantumBlack, or equivalent)
- Experience leading or mentoring data scientists and analytics professionals
We offer in return!
- Competitive salary & pension scheme, discretionary bonus scheme, 25 days annual leave plus ability to purchase 5 additional days, hybrid working options, Private Medical cover, Employee Share Purchase Plan, Life Assurance, Subsidised gym membership, Comprehensive Learning & development offerings, Employee Assistance program.
Integrity. client focus. respect. excellence. teamwork
Our core values dictate how we live and work. We’re an ethical and honest company that’s wholly committed to its clients. A business that’s engaged in mutual trust and respect for its employees and partners. A place where colleagues perform at the highest levels. And a working environment that’s collaborative and supportive.
Diversity & Inclusion
At Chubb, we consider our people our chief competitive advantage and as such we treat colleagues, candidates, clients, and business partners with equality, fairness and respect, regardless of their age, disability, race, religion or belief, gender, sexual orientation, marital status or family circumstances.
We are committed to ensuring our recruitment process is inclusive and accessible to all. If you have a disability or long-term condition (for example dyslexia, anxiety, autism, a mobility condition or hearing loss) and need us to make any reasonable adjustments, changes or do anything differently during the recruitment process, please let us know.
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