Fraud Prevention Analyst

**Why Join dLocal?
**dLocal is the financial infrastructure powering global commerce in the world's fastest-growing markets. The biggest companies in the world trust us to unlock growth in 60+ countries across emerging markets—moving money where others see complexity. We don't just process payments; we are architects of payment ecosystems and partners in our customers' expansion. You'll work alongside 1,300+ teammates from 40+ nationalities and tackle global challenges from day one.

 
   

What’s the opportunity?

As our Fraud Prevention Analyst II, you will help us shape fraud strategy across industries, balancing loss reduction with conversion and top-tier user experience. You’ll decide when to approve or reject payments, using data, experimentation, and close collaboration with Data Science to improve model features and thresholds. You’ll own KPIs for key clients and verticals, translating their needs into effective controls. A core part of your impact will be streamlining workflows and reducing manual review through smarter rules and tooling. Your work will directly lower chargebacks/claims while protecting revenue and customer trust.

 

What will I be doing?

• Continuously analyze transactional data to identify emerging fraud patterns and attack vectors.
• Design, test, and iterate rules that minimize fraud while safeguarding conversion and UX.
• Collaborate with Data Science to enhance features, refine model rules, and set optimal scoring thresholds.
• Evaluate and integrate new datasets to improve data quality, coverage, and signal relevance.
• Own KPIs for key clients and industries; meet with their fraud teams to align on goals and solutions.
• Run experiments (A/B, backtesting) to quantify rule/model impact and guide decisions.
• Build clear monitoring (dashboards/alerts) to track performance, drift, and false positives.
• Identify and drive automation opportunities that reduce manual reviews and errors (e.g., auto-tuning thresholds, rule deployment pipelines).
• Partner with Engineering/Data to implement tooling, integrations, and reporting that accelerate decisioning and incident response.

What skills do I need?

• Bachelor’s degree in Economics, Engineering, Statistics, or related field.
• 3+ years in fraud prevention or a closely related risk domain.
• Strong SQL and experience working with large datasets.
• Proven experience with AI/ML in fraud or risk scoring (features, thresholds, model-informed rules).
• Strong analytical, presentation, and communication skills; able to translate insights into action.