The Role

This is a senior, hands-on engineering leadership role responsible for turning pricing, portfolio and underwriting models into robust, production-grade capabilities embedded within operational workflows and core systems (e.g., PAS platforms such as Duck Creek, EXP).

You will define and implement the standards, patterns and architecture that ensure analytics solutions are scalable, monitored, auditable and commercially durable — across a portfolio of pricing, conversion and risk models serving Commercial Insurance across EMEA.

You will be the most senior technical practitioner in the analytics and AI team, responsible for setting the engineering bar and raising capability across a growing but junior-heavy squad. This is not a research or experimentation role. It is a build-and-scale role.

What You Will Join

You will join the EMEA Data, Analytics & AI team at Chubb — one of the world’s largest commercial insurers. The team delivers data, analytics and AI capabilities across Commercial Insurance in EMEA.

You will work alongside data scientists, data engineers, actuaries and underwriters — translating analytical models into production capabilities that directly impact commercial outcomes.

Why This Role Matters

This is not a support function. The models this team builds directly influence which risks Chubb underwrites, how they are priced, and how the portfolio is managed. Getting them into production reliably — and keeping them there — is a commercial priority, not a technical nice-to-have.

Core Responsibilities

1. Production-Grade ML & AI Deployment (Primary Accountability)

• Design and implement scalable deployment patterns for ML models (batch and API-based scoring)

• Establish model lifecycle standards: versioning, retraining triggers, monitoring, documentation

• Embed pricing, conversion and risk models into underwriting workflows and core platforms

• Define CI/CD standards for analytics delivery pipelines

• Ensure reproducibility and robustness of all deployed solutions

2. Model Monitoring & Governance

• Implement monitoring frameworks: model performance stability, drift detection (data & prediction), portfolio impact tracking

• Build monitoring dashboards for pricing and propensity models

• Partner with actuarial and risk teams on governance, audit readiness and model documentation

• Ensure compliance within regulated insurance environments

3. Analytics Engineering & Data Product Design

• Design curated datasets and reusable feature frameworks that serve multiple downstream models

• Define standards for analytics consumption layers supporting pricing monitoring, portfolio steering and conversion analysis

• Improve data reliability and engineering maturity across squads

• Guide technical design decisions across pricing and underwriting AI initiatives

4. Technical Leadership & Capability Uplift

• Provide hands-on technical leadership to data scientists and data engineers

• Conduct code reviews, pair programming and architectural oversight

• Raise engineering discipline across a team that is strong analytically but developing its engineering maturity

• Standardise development practices, tooling and ways of working

• Act as the technical authority on how models are built, tested and deployed

5. AI & Workflow Integration

• Operationalise AI-enabled use cases including document intelligence and workflow augmentation

• Ensure AI solutions are integrated into the realities of insurance systems and processes

• Define scalable deployment patterns for emerging AI initiatives (including GenAI)

Required Experience

• Proven experience in data science, ML engineering, or analytics engineering — with a clear trajectory toward production systems
• Proven experience deploying ML models into production — batch and/or real-time scoring in commercial environments
• Experience integrating analytics into operational workflows — not just dashboards, but embedded decision support
• Experience designing and operating model monitoring frameworks — drift detection, performance tracking, alerting
• Strong Python ecosystem expertise — production-quality code, not notebook-only
• Experience with ML lifecycle tooling — MLflow, Azure ML, SageMaker or equivalent
• Cloud platform experience — Azure preferred
• Experience working in regulated industries — insurance or financial services strongly preferred

Desirable

• Experience with insurance platforms (Duck Creek, Guidewire, Acturis)
• Experience with pricing or actuarial model deployment
• Familiarity with CI/CD for ML (MLOps pipelines, automated testing, model registries)
• Experience leading or mentoring junior engineers and data scientists
• Exposure to GenAI / LLM deployment in enterprise settings

We offer in return!

Competitive salary & pension scheme, discretionary bonus scheme, 25 days annual leave plus ability to purchase 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.

Required Experience

• Proven experience in data science, ML engineering, or analytics engineering — with a clear trajectory toward production systems
• Proven experience deploying ML models into production — batch and/or real-time scoring in commercial environments
• Experience integrating analytics into operational workflows — not just dashboards, but embedded decision support
• Experience designing and operating model monitoring frameworks — drift detection, performance tracking, alerting
• Strong Python ecosystem expertise — production-quality code, not notebook-only
• Experience with ML lifecycle tooling — MLflow, Azure ML, SageMaker or equivalent
• Cloud platform experience — Azure preferred
• Experience working in regulated industries — insurance or financial services strongly preferred

Desirable

• Experience with insurance platforms (Duck Creek, Guidewire, Acturis)
• Experience with pricing or actuarial model deployment
• Familiarity with CI/CD for ML (MLOps pipelines, automated testing, model registries)
• Experience leading or mentoring junior engineers and data scientists
• Exposure to GenAI / LLM deployment in enterprise settings