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.

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.

Solid understanding of the end-to-end data lifecycle — from raw data and pipelines to modeling and reporting — enough to collaborate effectively with Data Engineers, Data Analysts, and Data Scientists.

Demonstrated experience as a technical liaison or requirement-owner working with additional Data, Product and Engineering teams — someone who can push back on a delivery that doesn't meet the need, not just accept what's handed over.

Experience producing documentation that holds up under audit or regulatory scrutiny is a strong plus.

Proactive, collaborative mindset; comfortable operating with a high degree of autonomy on AML data analytics, as part of a small and growing team.