Technical practice
How would you identify and prioritize AI use cases across reinsurance operations?
What to coverThe role calls for translating operational pain points into scalable opportunities with measurable value. Suggested preparation: explain how you would compare expected impact, feasibility, risk, and the evidence needed to validate a priority.
How would you redesign an operational process before introducing AI?
What to coverThe posting emphasizes AI-driven process reengineering, not just deploying a model. Suggested preparation: discuss mapping the current workflow, identifying avoidable steps, and defining how the redesigned process would be measured.
When would you choose conventional machine learning, generative AI, or a non-AI process change?
What to coverThe role requires machine learning and generative AI expertise in service of operational problems. Suggested preparation: compare the approaches by task fit, available evidence, reliability, and implementation cost rather than assuming AI is always warranted.
How would you design and test prompts for a business-facing LLM solution?
What to coverHands-on LLM and prompt-engineering experience is stated in the posting. Suggested preparation: describe how you would specify the task, constrain outputs, test representative cases, and examine failures before release.
When would retrieval-augmented generation be useful, and how would you evaluate it?
What to coverRAG is named as a required area of expertise. Suggested preparation: explain the trade-off between retrieving relevant source material and adding system complexity, including checks for retrieval quality and whether answers are supported by that material.
How would you determine whether a generative AI solution is ready for production?
What to coverThe role spans ideation through production and calls for measurable business value. Suggested preparation: distinguish task-quality tests from operational measures such as reliability, review effort, and adoption, and explain how failures would affect a release decision.
What MLOps practices would you use to operate an AI solution after launch?
What to coverThe posting requests familiarity with MLOps practices and tools, without prescribing a toolset. Suggested preparation: cover versioning, monitoring, handling degraded performance, and assigning ownership for changes.
How would you address responsible AI and model risk throughout the lifecycle?
What to coverEstablishing governance and model-risk practices is an explicit responsibility. Suggested preparation: explain how you would identify risks, document decisions, set appropriate human review, and revisit controls as a solution changes.
How would you turn an AI concept into a prioritized product roadmap?
What to coverProduct vision, roadmaps, feature prioritization, and launches are listed as preferred experience. Suggested preparation: discuss sequencing a useful first release against dependencies, risk, and measurable outcomes; this is a practice topic, not a stated required methodology.
How would you assess an AI opportunity involving reinsurance analytics or catastrophe-modeling outputs?
What to coverFamiliarity with reinsurance analytics tools or platforms such as RMS, AIR, or CoreLogic is preferred, not required. Suggested preparation: focus on understanding the workflow, the meaning and limitations of its outputs, and where AI could add value without overstating its accuracy.
Behavioural practice
Tell me about a time you built consensus among senior stakeholders with different priorities.
What to coverThe role calls for influencing senior leaders and partnering across business and technology functions. Suggested preparation: use a real situation, describe how you surfaced disagreements and reached a decision, and state the result.
Describe a time you coordinated a cross-functional solution from idea to launch.
What to coverThe posting emphasizes execution across Broking, Distribution, Analytics, CSS/Operations, and IT. Suggested preparation: give a real example of the situation, your coordination and trade-off decisions, and the delivery outcome.
Tell me about a time you helped people adopt a significant process or technology change.
What to coverChange management, training materials, knowledge resources, and communities of practice are part of the role. Suggested preparation: describe the situation, what you did to address adoption barriers, and what changed as a result.
Describe a time you explained a complex technical approach to a nontechnical decision-maker.
What to coverThe role requires translating AI concepts into clear business value propositions. Suggested preparation: identify the audience and decision, explain how you framed the trade-offs, and give the outcome.
Tell me about a time you raised a risk that complicated a promising initiative.
What to coverResponsible AI, governance, and model-risk considerations are explicit responsibilities. Suggested preparation: use a real situation, describe how you communicated the concern and worked toward a decision, and explain the result without presenting an unverified personal achievement.