• Own the AI Product Lifecycle: Drive AI-native products from ideation through launch and iteration • Set Vision and Roadmap: Define a compelling vision for how AI agents will reshape GTM workflows • Engage Customers: Partner with sales, marketing, and CS users to identify where AI-driven automation delivers the most value • Measure What Matters: Define and track AI-centric metrics and turn insights into action • Champion the User: Ensure AI capabilities solve real user problems • Lead Cross-Functionally: Rally engineers, AI/ML specialists, designers, and GTM teams to ship best-in-class experiences • Align Stakeholders: Communicate AI product strategy clearly and compellingly to executives

• Knowledge of the systems they work with • Identify and map business needs in a practical and visual way. • Represent business needs as maps, business requirements and user stories; • Master the concepts of business analysis and process, user-centered design and agile methods. • Have facilitation and workshop facilitation skills using techniques such as: a. Design Thinking; b. Business Model Canvas; c. UX Design. • Knowledge of business analysis practices, MVP (Minimum Viable Product) and agile requirements; • Understand existing business processes, products and services and their rules in order to provide IT solutions whenever necessary; • Understand the needs of business managers and customers/users to propose solutions; • Knowledge of the methods: a. SCRUM/Lean; • preparation of the backlog of user stories for the start of sprint execution. • acceptance of stories built during the sprint.

• Own the roadmap for your product domain within A2A—maintaining a well-prioritized backlog, a clear rationale for every decision, and a delivery plan that the team can execute against • Define and prioritize features based on partner needs, transaction data, competitive signals, and business impact • Maintain continuity of existing Dwolla customer commitments while contributing to NMI integration milestones — balancing BAU stability with forward progress • Write clear, detailed product requirements and acceptance criteria; run scoping sessions with Engineering that result in shared understanding and no surprises • Lead product discovery: customer interviews, partner feedback sessions, competitive teardowns, and data analysis—then synthesize findings into a point of view • Build a deep understanding of the full A2A payment lifecycle—funds flow, return handling, exception processing, reconciliation, Reg-E obligations—and design products that hold up end-to-end • Partner with Engineering on technical design decisions, flagging tradeoffs and pushing for solutions that are scalable, not just shippable • Define success metrics for every initiative, track them post-launch, and share what you learned with the team • Build awareness of A2A capabilities across internal teams, helping Sales and Partner Success understand when and how to position bank-direct payment solutions • Collaborate with Legal, Compliance, Risk, and Payment Ops to ensure products are compliant and operationally sound before they ship • Support go-to-market: work with product marketing, sales, and customer success to drive adoption of new capabilities • Use AI tools actively in your workflow—discovery synthesis, spec drafting, hypothesis generation, competitive research—and bring that velocity to your team • Represent the product in customer workshops, partner calls, and internal stakeholder reviews with authority and clarity • Contribute to team OKRs; participate in quarterly planning and own your results

• Own and prioritise the product backlog across features, LiveOps initiatives, and player experience improvements • Translate business goals, player needs, and product strategy into actionable development plans • Work closely with Engineering and Design teams to deliver high-impact features efficiently • Ensure sprint goals and roadmap priorities remain aligned with commercial and engagement objectives • Use AI tools such as Claude, ChatGPT, Notion AI, and analytics copilots to accelerate product workflows • Generate and refine user stories, documentation, requirements, and experimentation plans using AI-assisted processes • Support rapid prototyping and validation of product ideas • Continuously identify opportunities where AI can improve team efficiency, product quality, and player outcomes • Drive data-informed product decisions using analytics, player insight, and A/B testing • Monitor and optimise KPIs including engagement, retention, conversion, and monetisation • Support iterative feature development rather than large one-off releases • Collaborate with analytics teams to identify opportunities for growth and optimisation • Facilitate Agile ceremonies including sprint planning, backlog refinement, and retrospectives • Communicate priorities, trade-offs, and delivery status clearly to stakeholders • Help align teams around measurable outcomes and player impact