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 (., 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)
1. Design and implement scalable deployment patterns for ML models (batch and API-based scoring)
2. Establish model lifecycle standards: versioning, retraining triggers, monitoring, documentation
3. Embed pricing, conversion and risk models into underwriting workflows and core platforms
4. Define CI/CD standards for analytics delivery pipelines
5. Ensure reproducibility and robustness of all deployed solutions
2. Model Monitoring & Governance
6. Implement monitoring frameworks: model performance stability, drift detection (data & prediction), portfolio impact tracking
7. Build monitoring dashboards for pricing and propensity models
8. Partner with actuarial and risk teams on governance, audit readiness and model documentation
9. Ensure compliance within regulated insurance environments
3. Analytics Engineering & Data Product Design
10. Design curated datasets and reusable feature frameworks that serve multiple downstream models
11. Define standards for analytics consumption layers supporting pricing monitoring, portfolio steering and conversion analysis
12. Improve data reliability and engineering maturity across squads
13. Guide technical design decisions across pricing and underwriting AI initiatives
4. Technical Leadership & Capability Uplift
14. Provide hands-on technical leadership to data scientists and data engineers
15. Conduct code reviews, pair programming and architectural oversight
16. Raise engineering discipline across a team that is strong analytically but developing its engineering maturity
17. Standardise development practices, tooling and ways of working
18. Act as the technical authority on how models are built, tested and deployed
5. AI & Workflow Integration
19. Operationalise AI-enabled use cases including document intelligence and workflow augmentation
20. Ensure AI solutions are integrated into the realities of insurance systems and processes
21. Define scalable deployment patterns for emerging AI initiatives (including GenAI)
Required Experience
22. Proven experience in data science, ML engineering, or analytics engineering — with a clear trajectory toward production systems
23. Proven experience deploying ML models into production — batch and/or real-time scoring in commercial environments
24. Experience integrating analytics into operational workflows — not just dashboards, but embedded decision support
25. Experience designing and operating model monitoring frameworks — drift detection, performance tracking, alerting
26. Strong Python ecosystem expertise — production-quality code, not notebook-only
27. Experience with ML lifecycle tooling — MLflow, Azure ML, SageMaker or equivalent
28. Cloud platform experience — Azure preferred
29. Experience working in regulated industries — insurance or financial services strongly preferred
Desirable
30. Experience with insurance platforms (Duck Creek, Guidewire, Acturis)
31. Experience with pricing or actuarial model deployment
32. Familiarity with CI/CD for ML (MLOps pipelines, automated testing, model registries)
33. Experience leading or mentoring junior engineers and data scientists
34. Exposure to GenAI / LLM deployment in enterprise settings