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 Do

• Team 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 Bring

• Proven experience in analytics within a complex risk, fintech, or payments environment

• Strong technical foundation in SQL and Python; experience with data modeling and automation

• Deep understanding of one or more risk areas: fraud, AML/CFT, credit risk

• Experience leading analysts or analytics engineers, with a track record of raising the bar

• Ability to zoom out and to zoom in to support implementation, often in the same day

• Skilled at stakeholder management, especially across technical and non-technical teams

• Business-aware, impact-driven, and curious