Jobs / other
Head of Engineering - Adyen protect
Adyen · Amsterdam
IND sponsor
What you will do
- Vision & Strategy: Define and execute the technical roadmap for Adyen Protect, aligning Machine Learning capabilities with the evolving needs of global e-commerce and regulatory landscapes.
- Scale ML Operations: Lead four multidisciplinary teams in building and maintaining scalable infrastructure for model training, real-time scoring, and automated experimentation (A/B testing) at the millisecond level.
- Engineering Excellence: Establish and maintain high standards for engineering quality and reliability. Ensure our fraud detection systems are resilient, low-latency, and capable of processing massive data volumes without downtime.
- Cross-Functional Leadership: Partner closely with Product Leaders, Risk officers, and other parts of the business to translate complex fraud patterns into robust technical features and automated mitigation strategies.
- Team Development: Mentor and grow a high-performing organization of engineers and engineering managers. Foster an inclusive environment that encourages rapid prototyping, ethical AI practices, and continuous learning.
- Merchant Advocacy: Interface with our largest global merchants to understand their unique risk profiles and ensure Adyen Protect provides the granular control and insights they need to grow their business safely.
What they look for
- ML & Data Enthusiast: You have a deep understanding of Machine Learning lifecycles, from data engineering and feature extraction to model deployment and monitoring.
- Proven Leader: You have a track record of leading complex engineering organizations and managing other managers, specifically within high-growth fintech or SaaS environments.
- Strategic Problem Solver: You can balance the immediate need for fraud mitigation with the long-term goal of reducing "false positives" to maximize merchant revenue.
- Tech-Fluent: You remain technically literate and are comfortable diving into architecture discussions regarding distributed systems, data pipelines, and real-time processing.
- Collaborative: You excel at building bridges between technical and non-technical stakeholders, explaining the "why" behind complex (ML) decisions.