Fraud Analytics & Strategy Manager

Fraud Strategy & Model Risk Analytics Manager, Fraud Analytics & Governance Duration: 12 months, likely extension Pay: $65 – $67 / hourly as W2 Location: Candidates need to work 3 days onsite each week in one of the following locations: Dallas/Irving (TX), New Castle (DE), Jacksonville (FL), Sioux Falls (SD). Role Summary As a key member of the Fraud Analytics, Modeling & Intelligence organization, you will design and execute data‑driven fraud strategies for North American and global portfolios. This high‑impact role focuses on the full fraud lifecycle—from preventing synthetic ID and application fraud to mitigating sophisticated account takeover (ATO) schemes. You will sit at the intersection of data science and risk management, leveraging large‑scale datasets to build proactive defenses that safeguard our customers and the bank’s reputation. Key Responsibilities Strategy & Execution: Lead Fraud Strategy: Design, test, and implement automated fraud risk strategies within advanced decision engines for consumer and commercial portfolios. Lifecycle Management: Manage the end‑to‑end model lifecycle, partnering with developers, vendors, and Model Risk Management (MRM) to ensure models are validated, high‑performing, and compliant. Innovation: Continuously evaluate new data sources, AI/ML capabilities, and external tools to stay ahead of evolving fraud patterns. Analytics & Insights: Advanced Data Mining: Extract actionable insights from Big Data environments (Hadoop, Hive, Python) to identify emerging attack vectors and behavioral trends. Reporting & Governance: Support the “Authorization Governance” process by monitoring historical performance and translating complex data into clear, executive‑level documentation. Technical Documentation: Produce rigorous technical reports that meet regulatory standards and survive deep‑deep audits from supervisory authorities. Collaboration & Leadership: Cross‑Functional Partnership: Act as a critical liaison between Fraud Policy, Operations, and Technology to ensure seamless execution of business priorities. Risk Advocacy: Foster a culture of transparency and ethical judgment, ensuring all strategic decisions align with global compliance and regulatory requirements. Qualifications Experience: 5+ years in Fraud, Payments, or Risk Analytics. Experience in the banking or fintech sector is highly preferred. Technical Proficiency: 3+ years of hands‑on experience in a Big Data environment. Advanced proficiency in Python, SQL, SAS, or Hive/Impala is required. Analytical Rigor: Proven ability to apply mathematical and statistical techniques to solve complex, real‑world business problems. Communication: Exceptional ability to “translate” technical findings into strategic narratives for senior leadership and regulatory bodies. Autonomy: A self‑starter capable of navigating a complex global matrix organization with minimal oversight. Required Core Fraud Analytics Experience Fraud Strategy Analytics – Building and evaluating fraud detection rules and strategies. Fraud Modelling – Developing and validating fraud risk models. Fraud Project Management – Leading or contributing to fraud‑related initiatives and programs within a financial services environment. Technical Requirements SAS or SQL (Required): Intermediate to advanced proficiency is a core requirement. Candidates should be comfortable performing model performance assessments, strategy monitoring, and data‑driven analysis. Excel (Required): Working knowledge of Excel for data manipulation, reporting and analysis. Preferred Industry Knowledge Payments & Card Products: Strong preference for candidates with knowledge of payments ecosystems, card products and related fraud vectors. Education Required: Bachelor’s Degree in a quantitative field (Statistics, Mathematics, Physics, Economics, or Computer Science). Preferred: Master’s Degree or Ph.D. in a related field. #J-18808-Ljbffr Aquent