Fraud Data Analyst - Barcelona

Please note that this opportunity requires relocation to our Barcelona office.

About us

Satispay began by rethinking the simple act of a payment to remove the friction from our daily routines. But we didn't stop there. Today, we are building a complete financial platform designed to empower people and concretely improve their lives. By giving our 6 million users a clear, open path to pay, save, and invest, we are evolving into the definitive destination for every financial need. What you'll be doing

As our Fraud Data Analyst, you'll be the person who secures the future of digital payments by architecting sophisticated real-time defense strategies. Here's what your day-to-day will look like:

• Architect Real-Time Strategies – You will be responsible for our real-time decisioning engine, designing and deploying logic that combines ML scores with velocity features to make millisecond decisions. - Precision Rule Development – Translate complex fraud patterns into high-performing rules, optimizing the balance between False Positive Rates (FPR) and Gross Fraud Loss. - Deconstruct Payment Fraud – Proactively hunt for emerging schemes (ATO, social engineering) and turn these insights into end-to-end mitigation strategies. - Optimize the Decisioning Stack – Partner with Product and Engineering to define analytical requirements for new features and third-party data signals. - – Work with Data Science to operationalize machine learning models, determining how to best "wrap" model scores with heuristic rules. - – Analyze security measures to identify unnecessary hurdles, designing dynamic friction strategies for a seamless user experience. - – Monitor rule efficacy and portfolio health, presenting data-driven recommendations to senior leadership to evolve our risk appetite. Expect challenges, collaboration, and the freedom to bring your ideas to life in a fast-moving and supportive environment. Things change quickly here, so be ready to adapt, take initiative, and shape your role as we grow.