Manager, Fraud Analytics & Governance

Manager, Fraud Analytics & Governance

Onsite 3 days each week in either New York City, Chicago, Jacksonville, FL or Sioux Falls.

Candidates need to work 3 days onsite each week in either New York City or Chicago.

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.

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.

The target hiring compensation range for this role is $62 to $67. Compensation is based on several factors including, but not limited to education, relevant work experience, relevant certifications, and location.

About Aquent Talent:

Aquent Talent connects the best talent in marketing, creative, and design with the world's biggest brands.

Our eligible talent get access to amazing benefits like subsidized health, vision, and dental plans, paid sick leave, and retirement plans with a match. We also offer free online training through Aquent Gymnasium. More information on our awesome benefits!

Aquent is an equal-opportunity employer. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, and other legally protected characteristics. We're about creating an inclusive environment-one where different backgrounds, experiences, and perspectives are valued, and everyone can contribute, grow their careers, and thrive.