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Adyen is building out its capabilities in data-driven risk management to combat financial crime across its platform. We operate at a massive global scale, enabling businesses around the world to accept payments, issue cards, and embed financial products, helping millions of business owners around the globe do business with less friction. While we have foundational elements in place, we have recently shifted our strategy to take a more holistic approach to proactively detect and mitigate financial crime.
Job Responsibility:
Lead and mentor multiple multidisciplinary Engineering Teams in Amsterdam focused on data-driven risk and financial crime products
Drive the technical strategy and execution for building scalable and resilient systems for financial crime detection and mitigation, covering the entire business lifecycle on the platform
Oversee the development and deployment of machine learning models and data pipelines for real-time and batch risk assessment
Explore and implement applications of cutting-edge AI technologies, including GenAI and LLMs, to automate manual investigation tasks, improve detection accuracy, and provide rich context for human reviewers and models
Collaborate closely with expert investigators and operations teams to understand their workflows, integrate human feedback into automated systems, and ensure tools meet their needs
Partner with Product Managers, Data Scientists, and other Engineering Teams globally to define product requirements, prioritize initiatives, and integrate risk capabilities into Adyen's platform
Ensure systems are built with robust monitoring, explainability, and governance frameworks to meet regulatory requirements and maintain high quality and fairness
Stay informed about the latest advancements in financial crime typologies, risk management techniques, and AI/ML technologies, applying relevant insights to Adyen's products
Requirements:
Proven experience leading and mentoring multiple and multi-disciplinary Engineering Teams
Significant experience in building and deploying data-driven products and systems at scale, ideally in a domain involving Risk, Fraud, or Financial Crime
Deep understanding of the machine learning lifecycle, from data ingestion and feature engineering to model training, deployment, monitoring, and experimentation
Strong technical background with experience in data processing, distributed systems, and relevant programming languages (e.g., Java, Python)
Familiarity with applying advanced AI techniques, such as Generative AI and Large Language Models, to solve real-world problems, particularly in automating complex workflows or analyzing unstructured data
Experience working closely with non-technical stakeholders, such as Operations Teams, Legal/Compliance experts, and Product Managers
Excellent communication skills with the ability to articulate complex technical concepts and strategies to diverse audiences
Ability to balance short-term needs with a long-term strategic vision
Experience in hiring and developing technical talent