Technical practice
How would you frame an operational problem before proposing an AI product?
What to coverThe role covers AI initiatives for platforms and internal operations. Suggested answer focus: define the user and workflow, establish the operational baseline, and identify what AI must improve before choosing an approach.
How would you prioritise an AI product roadmap from launch through optimisation?
What to coverThe posting assigns end-to-end ownership, including roadmap definition. Suggested answer focus: compare expected impact, feasibility, dependencies and execution risk, then explain what evidence would change the priority after launch.
What requirements would you define with engineering, data science and business teams for a scalable AI solution?
What to coverThe role explicitly partners with all three functions to design scalable solutions. Suggested answer focus: clarify the workflow, model capabilities and limitations, operational constraints, ownership and measurable acceptance criteria.
How would you evaluate both AI performance and business impact?
What to coverThe posting calls for success metrics and evaluation frameworks covering both outcomes. Suggested answer focus: distinguish model-level measures from operational KPIs, set a baseline, and explain how you would interpret a result where one improves but the other does not.
How would you investigate a decline in an AI-enabled product’s performance after launch?
What to coverThe role includes monitoring and optimisation, and the Operations team analyses KPI fluctuations and root causes. Suggested answer focus: check whether the change reflects the model, its inputs, the workflow or measurement, then describe how findings would guide a response.
How would you design governance and human-in-the-loop controls for an AI product?
What to coverThe posting calls for reliable, responsible deployment with governance and human-in-the-loop processes. Suggested answer focus: explain which decisions warrant human review, how exceptions would be surfaced, and the trade-off between oversight and speed.
How would you adapt an AI product for use across regional and local teams?
What to coverThe role involves cross-market alignment, and the requirements mention scaling AI products across markets. Suggested answer focus: separate shared product components from market-specific needs and describe how you would evaluate performance and manage rollout dependencies in each market.
How would you assess whether an internal AI tool improves productivity?
What to coverThe team describes productivity as an AI opportunity, and the posting mentions internal tools. Suggested answer focus: define the workflow and baseline, measure adoption alongside time or quality outcomes, and check whether human review offsets apparent gains.
What would you put in an executive dashboard about an AI initiative?
What to coverThe role requires dashboards and executive-ready insights explaining outcomes and trade-offs. Suggested answer focus: present progress against agreed metrics, business impact, reliability signals and key decisions or risks without obscuring uncertainty.
How would you decide whether a new generative AI capability merits a product opportunity?
What to coverThe posting asks this role to assess AI and GenAI advances for relevance. Suggested answer focus: start with a defined problem, test capability and limitations against existing options, and weigh expected impact against reliability and deployment considerations.
Behavioural practice
Tell me about a time you aligned business and technical stakeholders on a product decision.
What to coverThis reflects the role’s cross-functional partnerships. Suggested preparation: use a real situation, describe how you clarified competing needs and reached a decision, and state the result.
Tell me about a time you managed dependencies across teams or markets.
What to coverThe posting calls for cross-market alignment and execution-risk management. Suggested preparation: explain the situation, your actions to surface and resolve dependencies, and the resulting delivery outcome.
Tell me about a time you explained a difficult trade-off to senior stakeholders.
What to coverThe role includes executive-ready reporting and communication of AI outcomes and trade-offs. Suggested preparation: give the context, show how you made the evidence and uncertainty clear, and describe the decision or result.
Tell me about a time performance data led you to change a product or operational plan.
What to coverThe posting emphasises metrics, interpretation and optimisation. Suggested preparation: describe the initial signal, how you investigated it, the action you took and the measured result.
Tell me about a time you owned an initiative from problem definition through launch.
What to coverThe advertised role has end-to-end product and initiative ownership. Suggested preparation: use a real example to explain your decisions at each stage, how you handled a delivery risk and what happened after launch.