Fraud Model Analyst

SoFi SoFi · Fintech · Frisco, TX · Member Service Delivery Strategy

This role focuses on the governance, oversight, and lifecycle management of third-party fraud models within a fintech company. The Fraud Model Analyst will ensure vendor models are compliant, well-documented, and effectively monitored, partnering with various internal teams (MRM, Legal, Compliance, Fraud Strategy) and external vendors. Responsibilities include managing the model lifecycle, analyzing performance, investigating issues using SQL and Python, supporting integration with internal models, and preparing documentation and audit responses. The role requires experience in fraud, risk analytics, or model governance, with proficiency in SQL and Python, and knowledge of MRM frameworks.

What you'd actually do

  1. Managing the end-to-end lifecycle of vendor fraud models, including onboarding, documentation, monitoring, and periodic reviews
  2. Partnering with Model Risk Management (MRM), Legal, and Compliance teams to ensure adherence to governance and regulatory requirements
  3. Coordinating with external vendors to obtain model documentation, technical details, and performance insights
  4. Analyzing model performance metrics (e.g., fraud capture, false positive rates, drift) and identifying risks or improvement opportunities
  5. Investigating model behavior and data issues using SQL and internal datasets to support root cause analysis

Skills

Required

  • 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

What the JD emphasized

  • governance
  • regulatory requirements
  • model performance metrics
  • documentation

Other signals

  • governance
  • oversight
  • lifecycle management
  • third-party (vendor) fraud models
  • compliance
  • regulatory requirements
  • model performance metrics
  • SQL
  • Python
  • documentation