Senior Optimization Data Analyst

Job description

This is Adyen Adyen provides payments, data, and financial products in a single solution for customers like Meta, Uber, H&M, and Microsoft - making us the financial technology platform of choice. At Adyen, everything we do is engineered for ambition. For our teams, we create an environment with opportunities for our people to succeed, backed by the culture and support to ensure they are enabled to truly own their careers. We are motivated individuals who tackle unique technical challenges at scale and solve them as a team. Together, we deliver innovative and ethical solutions that help businesses achieve their ambitions faster. Senior Optimization Data Analyst We are looking for a Senior Optimization Data Analyst to take a defining role in how Adyen drives payment performance for its most complex merchants. As a senior member of the Performance Optimization team, you will bring the full breadth of payment optimization expertise expected at this level, across authentication, authorization, and cost, while going deeper in risk, fraud, and ML-driven decisioning than any other role on the team. You will combine hands-on technical capability with recognized domain expertise in risk to lead the team's most complex merchant engagements, shape PO's analytical methodology, and accelerate the adoption of Adyen's optimization capabilities across the merchant portfolio. This role sits at the intersection of technical depth and commercial impact. You will work directly with large-scale transactional data, including ML output and labeled risk data, to generate comprehensive insights that connect to merchant strategy. You will do this in close collaboration with Adyen's product teams, through insights and feedback that drive product improvements, and an analytical layer that makes risk model behavior more interpretable and actionable for merchants and internal teams alike. You will build the frameworks, tools, and knowledge that allow the broader PO team and commercial stakeholders to operate with greater confidence and independence in this domain. The ideal candidate has a track record of driving measurable outcomes through data analytics in payments or fintech, with deep hands-on experience working with large-scale transactional data, and ML-based risk data. They operate with a high degree of autonomy, owning complex analytical problems end to end from scoping through to recommendation, without requiring active direction to make progress. And they bring the ability to translate what the data reveals into strategies that merchants and internal teams can understand and act on. What you'll do Domain-Led Merchant Engagement: Lead PO's most complex merchant engagements in the risk, fraud, and ML optimization domain. This includes designing and interpreting sophisticated analyses, advising merchants on how to safely operationalize ML-driven decisioning systems, identifying performance gaps that require domain-level expertise to diagnose, and driving strategic improvements in fraud strategy, risk rule configuration, authentication flows, and authorization performance across Adyen's largest customers. Risk Analysis and Insight Generation: Work directly with large-scale risk model data, including ML output and labeled risk data, to analyze model behavior, detect performance patterns, and generate insights across merchant segments, traffic types, and risk profiles. In close collaboration with Adyen's product teams, contribute to building an analytical layer that connects model behavior to merchant strategy, improving explainability and enabling more targeted optimization. This work requires the ability to go beyond standard analytics and work with raw, complex data to surface what is not yet visible. Product Collaboration and Portfolio Intelligence: Work closely with Adyen's risk product teams to contribute structured, data-backed insights based on patterns observed across the merchant portfolio. Identify where risk model configurations produce inconsistent outcomes across segments, where merchant strategies diverge from model behavior, and where analytical findings can inform product priorities, as well as ensure merchants are equipped to configure, trust, and act on model-driven recommendations. This contribution operates at the portfolio level, going beyond the level of individual merchant escalations, and is grounded in data, rather than isolated merchant observations. Analytical Excellence and Methodology Ownership: Drive the quality and rigor of PO's analytical work in the risk and frau