Gather and document requirements from practice leads in Audit, Tax and Advisory.
Write product specifications for AI features: user stories, acceptance criteria, edge cases and success metrics.
Maintain and prioritise the product backlog across all active CoE initiatives.
Work with AI Engineers and Backend Engineers to scope features and define MVP boundaries.
Track delivery progress, surface blockers and coordinate resolution across engineering and business teams.
Define and monitor product success metrics adoption rates, task completion, time saved, error reduction.
Manage stakeholder communication: status updates, release notes, demo sessions with practice teams.
Coordinate user acceptance testing with practice teams before feature releases.
Document product decisions and rationale for audit trail purposes.
Required Skills
Requirements elicitation ability to interview domain experts (auditors, tax professionals) and translate their needs into precise written specifications
Product backlog management experience maintaining and prioritising a backlog using Jira, Azure DevOps or equivalent
Stakeholder management experience working with multiple business functions and managing competing priorities
Written communication ability to write clear, unambiguous product specifications that engineering teams can work from without verbal clarification
Basic understanding of how AI/ML systems work enough to know what is feasible, what is not, and what questions to ask engineers
Data literacy ability to read dashboards, interpret usage metrics and draw conclusions about product performance
