Aml Detection Platform H/F - Collective Works

1. Product Strategy and Roadmap

- Define and maintain the product vision, strategy and roadmap in alignment with the organisation's AML objectives.

- Translate business priorities, regulatory requirements and strategic objectives into clear product capabilities and delivery milestones.

- Identify opportunities to improve detection effectiveness, operational efficiency, scalability and explainability through the use of data, AI and automation.

- Prioritise product features based on business value, risk reduction, technical feasibility, delivery effort and stakeholder needs.

- Drive the transition from proof of concept to a robust, scalable and production-ready platform.

2. Product Ownership and Delivery

- Own and manage the product backlog, ensuring requirements are clearly defined, prioritised and actionable.

- Translate business needs into epics, features, user stories and acceptance criteria.

- Lead backlog refinement, sprint planning, reviews and other Agile ceremonies in collaboration with the delivery team.

- Coordinate dependencies across engineering, data, analytics, AI/ML and upstream/downstream systems.

- Monitor delivery progress, manage risks and impediments, and ensure commitments remain aligned with product priorities.

- Ensure that delivered capabilities meet functional, technical, operational and non-functional requirements.

- Establish clear success criteria and measurable KPIs to track product adoption, delivery performance and business outcomes.

3. AI/ML and Data Product Capabilities

- Work closely with Data Scientists and ML Engineers to translate analytical approaches into deployable product capabilities.

- Understand the end-to-end machine learning lifecycle, including data preparation, feature engineering, model development, testing, validation, deployment, monitoring and retraining.

- Support the definition of capabilities for experimenting with different models, comparing performance and promoting validated models into production.

- Facilitate the definition of appropriate evaluation metrics, including detection effectiveness, precision, recall, false-positive rates and operational impact, according to the use case.

- Ensure AI/ML capabilities are designed with appropriate levels of transparency, explainability, governance and human oversight.

The Product Owner is not expected to develop ML models directly, but must possess sufficient technical understanding to challenge assumptions, facilitate decisions and translate complex analytical concepts into product requirements.

4. Stakeholder and Team Leadership

- Act as the primary interface between business stakeholders, Compliance, Data Scientists, Engineers, Architects and other relevant teams.

- Facilitate discussions and decision-making across stakeholders with different technical backgrounds and business objectives.

- Challenge requirements constructively, resolve competing priorities and drive consensus on product direction.

- Communicate progress, risks, trade-offs and decisions clearly to senior management and governance forums.