Fraud Strategy & Model Risk Analytics

Fraud Strategy & Model Risk Analytics
Manager, Fraud Analytics & Governance

Duration: 12 months, likely extension

Pay: $65 - $67 / hourly as W2

Candidates need to work 3 days onsite each week in one of the below 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 include:

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-dive 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:

Candidates should have a strong foundation in Fraud Analytics, with experience in one or more of the following areas:

• 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.

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