Lead Fraud Analytics Implementation Manager

Aquent is seeking exceptional talent to partner with a leading global financial institution, a trailblazer at the forefront of innovation and dedicated to securing the global financial ecosystem. This organization plays a critical role in enabling seamless and secure transactions worldwide, impacting millions of lives and businesses daily. Join a team committed to excellence, constantly pushing the boundaries of what's possible in financial technology, and make a profound impact on global financial security.

Are you a visionary leader with a passion for data science and a knack for turning complex models into real-world solutions? We are seeking a dynamic individual to drive the implementation of advanced fraud detection models within a vast enterprise financial environment. In this pivotal role, you will be the crucial link between cutting-edge data science and operational execution, directly safeguarding financial systems and customers. Your expertise will not only optimize defenses against fraud but also significantly contribute to the security and integrity of global financial transactions.

What You'll Do

• Independently evaluate, benchmark, and test 4-5+ machine learning models, including those from external partners, assessing their performance, business impact, and return on investment to guide strategic prioritization.
• Lead the comprehensive, end-to-end deployment of fraud detection models into our operational ecosystem, ensuring seamless integration and functionality.
• Collaborate closely with internal governance teams to navigate and secure model validation, thorough documentation, and formal regulatory and internal approvals.
• Direct cross-functional technical roadmaps, harmonizing data science execution, infrastructure needs, and rules engine updates for smooth and effective model integration.
• Partner with fraud operations and rule-building teams to strategically embed model scores into active fraud rules and critical payment execution pathways.
• Act as the primary liaison, fostering strong communication and alignment between data science teams, technology partners, governance groups, and business risk executives.

Must-Have Qualifications

• Proven hands-on expertise in machine learning algorithms, deep understanding of model performance metrics (e.g., ROC-AUC, Precision/Recall, Population Stability Index), and feature evaluation techniques.
• Demonstrated experience in leading complex, multi-stakeholder technical deployments and managing the full lifecycle of model integration within large-scale organizations.
• Direct experience navigating compliance, governance frameworks, model validation, and approval processes specifically within the financial services sector.
• Profound ability to analyze extensive transaction and risk datasets, translating insights into actionable strategies for operational decision-making.

Nice-to-Have Qualifications

• Prior experience in fraud analytics, developing fraud rules, understanding payment transaction flows, or managing card issuer risk.
• Familiarity with network mandates, issuer-side implementations, or enterprise fraud solutions.
• Experience gained within large-scale financial institutions or complex banking environments.