Lead, Financial Crime Data Science & Data Engineering

Lead, Financial Crime Data Science & Data Engineering

• Team Product and Technology Development
• Location Atlanta, Georgia, United States

Make your mark at one of the biggest names in payments. We are seeking a hands‑on, execution‑focused Lead of Financial Crime Data Science & Data Engineering to build and advance the capabilities that power our transaction monitoring program and help shape the future of global commerce.

• Own detection logic performance (rule‑based and model‑driven), ensuring effectiveness across fraud, credit, and AML monitoring.
• Define and refine detection strategies based on emerging fraud typologies, regulatory requirements, and operational outcomes.
• Establish and monitor performance metrics (precision, recall, false positives, alert quality), driving measurable improvements.
• Influence trade‑off decisions between detection coverage, false positives, and operational cost, aligned to risk appetite.

• Lead the development, validation, and deployment of detection logic (rule‑based and model‑driven), ensuring delivery through data science and ML engineering teams.
• Direct analytical efforts to identify emerging fraud patterns and translate insights into implemented detection logic improvements.
• Ensure detection approaches balance statistical rigor, explainability, and operational usability.
• Support the detection logic lifecycle, including monitoring, retraining, and performance optimization.

• Lead the design and ensure delivery of scalable batch and real‑time data pipelines supporting detection logic and analytics.
• Define and enforce standards for data quality, validation, lineage, and pipeline reliability.
• Ensure data engineering capabilities support detection performance, regulatory reporting, and model lifecycle requirements.
• Partner with platform and engineering teams to deliver infrastructure aligned with detection logic needs.
• Drive execution discipline across data engineering work streams, ensuring clear ownership, timelines, and delivery accountability.

• Own the end‑to‑end lifecycle for detection logic (rule‑based controls and model‑driven signals), including design, prioritization, testing, deployment, and optimization.
• Ensure implementation is delivered through structured processes with clear ownership, controls, and timelines.
• Drive continuous refinement of detection logic using performance data, investigation outcomes, and emerging risk signals.
• Maintain alignment between detection logic and operational workflows.

• Define and ensure adherence to governance standards for detection logic, including documentation, validation, and change management.
• Support compliance with regulatory expectations (BSA/AML, OFAC, FinCEN, SR 11‑7).
• Partner with Model Risk Management, Compliance, and Internal Audit to support validation and regulatory reviews.
• Ensure detection approaches meet explainability and auditability standards required for regulatory scrutiny.

• Serve as the primary technical partner to Fraud Operations, Compliance, and Technology teams.
• Translate regulatory and operational requirements into detection logic, data priorities, and execution plans.
• Drive alignment across teams to enable effective implementation of detection capabilities.
• Influence upstream decisions in data, product, and platform domains that impact detection performance.

• Lead and develop a team of data scientists, data engineers, and ML engineers.
• Establish clear priorities, performance expectations, and accountability for delivery.
• Provide technical guidance and mentorship while enabling team members to own execution.
• Build and strengthen capabilities across detection modeling, data engineering, and analytics.

• 7+ years of experience in data science, machine learning, or data engineering.
• Proven experience leading teams in fraud detection, AML transaction monitoring, or credit risk.
• Demonstrated experience delivering detection logic…