Lead, Financial Crime Data Science & Data Engineering

Worldpay•Atlanta, GA

10d

About The Position

Ready to take your career global? 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.

Requirements

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 (rule-based and model-driven) in production environments.
Experience working in regulated financial services or fintech environments preferred.
Exposure to model risk management frameworks (e.g., SR 11-7) and regulatory interactions.

Nice To Haves

Payments experience
Masters degree or higher in Data Science or Data Engineering

Responsibilities

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 tradeoff 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 workstreams, 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.