Analytics Lead (NL)

We’re looking for an Analytics Lead to lead the analytics efforts in our Merchant Monitoring domain. This domain covers fraud risk, transaction monitoring (AML/CFT), and credit risk, focusing on scalable, automated, and high-impact risk decisioning.Within the domain, you will leverage your strong technical skills and hands-on knowledge of Mollie’s complex data flows to solve concrete business challenges. You’ll be responsible for shaping the analytics strategy, guiding embedded analysts (approximately 30% of the role), and driving impactful solutions through hands-on work (approximately 70% of the role) in close collaboration with engineering, product, and operations.What You’ll DoTeam Leadership: Guide and mentor data analysts and analytics engineers, owning the analytics roadmap for the Merchant Monitoring domain and ensuring high standards for quality, delivery, and impact. Data product development: Build and maintain clean, robust, performant, and user-friendly data products, decision-making tools, dashboards to monitor key metrics, fraud signals, transaction monitoring rules and alerts, and credit exposure. Data Analysis & interpretation: Analyze typologies, merchant behavior, transactional data to identify patterns, anomalies, and potential fraud indicators, utilizing data mining techniques to uncover key insights and inform risk strategies.Data Visualization & Storytelling: Create compelling data visualizations and narratives to communicate complex data insights effectively to both technical and non-technical audiences through various mediums (presentations, blog posts, etc.). Cross-functional Collaboration: Act as the main analytics point of contact for the Merchant Monitoring domain, collaborating cross-functionally with various teams to implement fraud prevention measures and improve processes. Craft Leadership: Promote data best practices within the domain, balancing long-term health of data assets with short-term needs.What You BringProven experience in analytics within a complex risk, fintech, or payments environmentStrong technical foundation in SQL and Python; experience with data modeling and automationDeep understanding of one or more risk areas: fraud, AML/CFT, credit riskExperience leading analysts or analytics engineers, with a track record of raising the barAbility to zoom out and to zoom in to support implementation, often in the same daySkilled at stakeholder management, especially across technical and non-technical teamsBusiness-aware, impact-driven, and curious

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