The Fraud Model Analyst will help  scale and govern vendor fraud models by:

• Managing the end-to-end lifecycle of vendor fraud models, including onboarding, documentation, monitoring, and periodic reviews
• Partnering with Model Risk Management (MRM), Legal, and Compliance teams to ensure adherence to governance and regulatory requirements
• Coordinating with external vendors to obtain model documentation, technical details, and performance insights
• Analyzing model performance metrics (e.g., fraud capture, false positive rates, drift) and identifying risks or improvement opportunities
• Investigating model behavior and data issues using SQL and internal datasets to support root cause analysis
• Supporting fraud model development initiatives by contributing to feature analysis, performance benchmarking, and strategy design
• Collaborating with Fraud Strategy, Data Science, and Engineering teams to integrate vendor models into fraud decisioning frameworks
• Preparing and maintaining model documentation, validation materials, and audit responses
• Supporting ongoing monitoring and reporting of vendor model performance, including identifying degradation and recommending actions
• Acting as a bridge between Data Science, Engineering, Fraud Strategy, and Risk/Compliance teams to ensure alignment
• Managing multiple models and timelines, ensuring timely delivery of governance and reporting requirements

What you’ll need:

• 3–5 years of experience in fraud, risk analytics, model governance, or related roles
• Bachelor’s degree in a quantitative field (e.g., Statistics, Mathematics, Economics, Engineering, Computer Science) or equivalent experience
• Working knowledge of Model Risk Management (MRM) frameworks and model governance processes
• Strong analytical skills with experience evaluating model performance and identifying issues
• Proficiency in SQL and Python for data analysis and investigation
• Experience working with fraud model performance metrics (e.g., fraud capture rate, false positive rate, precision/recall, AUC, drift monitoring)
• Familiarity with data science workflows and ability to work with datasets to support model analysis and validation
• Experience working with cross-functional stakeholders and external partners/vendors
• Strong documentation skills, including experience preparing model documentation, monitoring reports, or audit responses
• Clear communication skills with the ability to translate technical concepts into business and compliance context
• Strong organizational and program management skills, with the ability to manage multiple priorities

Nice to have:

• Experience working with fraud models or contributing to fraud model development
• Familiarity with machine learning concepts and ability to interpret model outputs and performance tradeoffs
• Prior experience working with vendor models (e.g., identity, device, or fraud risk vendors)
• Exposure to regulatory/compliance environments in financial services
• Experience with model monitoring frameworks or tools

• Are you interested in this position?

   

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