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