About this role
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Who we are:
Shape a brighter financial future with us.
Together with our members, we’re changing the way people think about and interact with personal finance.
We’re a next-generation financial services company and national bank using innovative, mobile-first technology to help our millions of members reach their goals. The industry is going through an unprecedented transformation, and we’re at the forefront. We’re proud to come to work every day knowing that what we do has a direct impact on people’s lives, with our core values guiding us every step of the way. Join us to invest in yourself, your career, and the financial world.
The role
SoFi is seeking a Fraud Model Developer to join our Fraud Model Development team. In this role, you will develop, evaluate, and monitor machine learning models that support data-driven fraud and risk decisions across SoFi’s products and services, including Personal Loans, Student Loans, Credit Cards, and Crypto.
You will build quantitative and machine learning solutions designed to reduce fraud losses, minimize false positives, lower operational costs, and protect SoFi members. You will also analyze model and product performance, identify key drivers of fraud losses, and translate complex findings into actionable recommendations for business and risk partners.
This role requires strong experience in machine learning, statistical modeling, data analysis, and model performance monitoring. You will work closely with Fraud Risk, Fraud Operations, Product, Engineering, Finance, Accounting, and other business teams to develop scalable fraud-modeling solutions and ensure model performance and loss trends are clearly communicated.
What you’ll do
• Develop quantitative, statistical, and machine learning models that reduce fraud losses, minimize false positives, and lower operational expenses associated with fraud complaints and disputes.
• Aggregate, clean, and synthesize large datasets from multiple data environments to support model development and analysis.
• Analyze complex datasets to identify fraud patterns, product-performance trends, and key drivers of losses across SoFi’s products.
• Design, test, validate, and recalibrate fraud models using appropriate statistical and machine learning methodologies.
• Monitor model performance and identify model degradation, data drift, or changes in fraud behavior.
• Conduct fraud-loss forecasting, sensitivity analyses, and scenario-based assessments to evaluate potential business impact.
• Automate recurring model-monitoring processes, analytical reporting, and dashboards.
• Investigate external risk data and industry trends to identify emerging fraud patterns and modeling opportunities.
• Partner with Engineering and machine learning platform teams to support model implementation and production deployment.
• Collaborate with Business Units, Operations, Product, Capital Markets, Finance, Accounting, and Risk partners to