Why should you join dLocal?
dLocal enables the biggest companies in the world to collect payments in 40 countries in emerging markets. Global brands rely on us to increase conversion rates and simplify payment expansion effortlessly. As both a payments processor and a merchant of record where we operate, we make it possible for our merchants to make inroads into the world’s fastest-growing, emerging markets.
By joining us you will be a part of an amazing global team that makes it all happen. Being a part of dLocal means working with 1000+ teammates from 30+ different nationalities and developing an international career that impacts millions of people’s daily lives. We are builders, we never run from a challenge, we are customer-centric, and if this sounds like you, we know you will thrive in our team.
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.
Automation mindset: comfortable with tools and workflows; familiarity with AWS SageMaker, Athena, or QuickSight is a plus.
Advanced English (mandatory).
