Fraud Model Analyst

The role:

We are looking for a Fraud Model Analyst to join our Fraud Model Development team, with a focus on governance, oversight, and lifecycle management of third-party (vendor) fraud models. This role will be responsible for ensuring vendor models are compliant, well-documented, and effectively monitored within SoFi’s fraud ecosystem.

This individual will partner closely with Model Risk Management (MRM), Legal, Compliance, Fraud Strategy, and external vendors to support onboarding, validation, and ongoing monitoring of vendor models. The role will also contribute to improving fraud decisioning by combining insights from vendor models with internally developed models and strategies.

The ideal candidate has a strong analytical mindset, is comfortable working with data, and can effectively collaborate across technical, business, and risk/compliance stakeholders.

By joining SoFi, you'll become part of a forward-thinking company that is transforming financial services for the better. We offer the excitement of a rapidly growing startup with the stability of an industry leading leadership team.

What you’ll do:

The Fraud Model Analyst will help SoFi 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