Senior Product Owner - AML Detection Platform

Context

A leading financial institution is seeking an experienced Senior Product Owner to drive the development and evolution of an innovative Anti-Money Laundering (AML) Detection Platform. This platform combines traditional rule-based detection capabilities with advanced Artificial Intelligence (AI) and Machine Learning (ML) models. The platform aims to transform AML detection by providing a flexible, modular, and scalable environment where business experts, data scientists, and technology teams collaborate to deliver effective detection strategies.

The successful candidate will work within the CIB - Client Engagement & Financial Security IT division, reporting to the Detection Product Manager. The role is based in Paris.

Missions

• Define and maintain the product vision, strategy, and roadmap aligned with the organization’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 using data, AI, and automation.
• Prioritize 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.
• Own and manage the product backlog, ensuring requirements are clearly defined, prioritized, 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 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.
• 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.
• Ensure AI/ML capabilities are designed with appropriate transparency, explainability, governance, and human oversight.
• 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.

Tools & Environment

• Agile delivery methodologies
• AI and Machine Learning models and frameworks
• Data analytics and feature engineering tools
• AML detection and transaction monitoring systems
• Cross-functional collaboration with Data Science, Engineering, Architecture, Model Risk, and Compliance teams

Working Conditions

• Location: Paris
• Reporting to: Detection Product Manager
• Division: CIB - Client Engagement & Financial Security IT

1. Significant experience as a Product Owner, Product Manager or equivalent delivery leadership role within financial services
2. Proven experience delivering complex data-intensive platforms, preferably involving AI/ML, analytics or data science
3. Solid experience working in Agile delivery environments, with the ability to manage product backlogs, prioritisation, dependencies and delivery commitments
4. Proven ability to engage senior business and technology stakeholders and drive decisions in complex organisations
5. Good understanding of AI/ML concepts and their application to AML detection
6. Familiarity with supervised and unsupervised learning, anomaly detection, classification, feature engineering and model evaluation
7. Understanding of the model lifecycle and the operational challenges involved in deploying and maintaining ML models in production
8. Understanding of AML and transaction monitoring detection processes
9. Understanding of detection typologies, alert generation, false-positive reduction and the operational challenges faced by AML investigation teams
10. Knowledge of the regulatory and governance environment surrounding AML controls and model usage in financial services