This list contains only the countries for which job offers have been published in the selected language (e.g., in the French version, only job offers written in French are displayed, and in the English version, only those in English).
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.
Job Responsibility
Design, test, and implement automated fraud risk strategies within advanced decision engines for consumer and commercial portfolios
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
Continuously evaluate new data sources, AI/ML capabilities, and external tools to stay ahead of evolving fraud patterns
Extract actionable insights from Big Data environments (Hadoop, Hive, Python) to identify emerging attack vectors and behavioral trends
Support the Authorization Governance process by monitoring historical performance and translating complex data into clear, executive-level documentation
Produce rigorous technical reports that meet regulatory standards and survive deep-dive audits from supervisory authorities
Act as a critical liaison between Fraud Policy, Operations, and Technology to ensure seamless execution of business priorities
Foster a culture of transparency and ethical judgment, ensuring all strategic decisions align with global compliance and regulatory requirements
Requirements
5+ years in Fraud, Payments, or Risk Analytics
Experience in the banking or fintech sector is highly preferred
3+ years of hands-on experience in a Big Data environment
Advanced proficiency in Python, SQL, SAS, or Hive/Impala is required
Proven ability to apply mathematical and statistical techniques to solve complex, real-world business problems
Exceptional ability to translate technical findings into strategic narratives for senior leadership and regulatory bodies
A self-starter capable of navigating a complex global matrix organization with minimal oversight
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