Data Specialist

**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 will you be doing?

• Own the end-to-end analytical lifecycle of our rule-based transaction monitoring systems, from data pulling and exploration in Databricks to proposing rule and threshold changes in alignment with Compliance Leadership requirements.

• Maintain and refine our false negative methodology — how we identify what monitoring is missing, not just what it catches.

• Help shape the agentic monitoring function — from orchestration design to implementation — as we expand automation across our AML/CFT monitoring flows.

• Act as the technical liaison with data scientists for ML driven detection: gather and document requirements, challenge and validate models delivered, and ensure outputs are usable for compliance decision-making.

• Own regulatory reporting for AML/CFT transaction monitoring, ensuring the underlying data is accurate, complete, and traceable — proactively identifying and resolving data quality issues before they become reporting or audit problems.

• Build and maintain audit-ready documentation for every methodology change — rationale that can be defended to a regulator or auditor months after the decision was made, by someone who wasn't in the room.

• Engage with Compliance Leadership to translate evolving AML/CFT regulatory requirements into concrete data and monitoring needs.

• Generate reports and dashboards that communicate overall Framework performance and monitoring effectiveness to both technical and non-technical stakeholders.

What skills do you need?

• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Engineering, or proven experience in a correlated field.

• 5+ years of experience in data analytics, risk, or fraud/financial crime analytics, ideally in financial services or fintech. Deep AML/CFT transaction monitoring experience is a strong plus but not mandatory.

• Strong SQL and Python (Databricks environment preferred); comfort with statistical analysis — this is not exclusively a data scientist role, so ML frameworks are a plus, not a requirement.