Job Details

The Risk Data Science team is looking for a Sr Staff Data Scientist to develop advanced machine learning models, guide measurement, strategy, and data-driven decision making to support credit underwriting for unsecured loans. The Sr Staff Data Scientist will work closely with Credit Risk, Product, Engineering, and Operations teams to design solutions and to innovate and continuously improve the unsecured loan underwriting models and enhance the loan origination process. These tasks involve researching, developing, and applying state of the art machine learning modeling methodologies to solve complex business problems. This role is very rewarding as your work will have a direct and immediate impact on the business' profitability.

• Research, develop, implement, and continuously improve machine learning models and strategies that support various credit underwriting for SoFi's unsecured lending businesses
• Proactively identify opportunities to apply advanced machine learning and AI approaches (e.g., GenAI, Agentic Systems, Graph Modeling, etc) to solve complex business problems
• Contribute to the continuous improvement of internal modeling codebase to support rigorous, efficient, scalable, and consistent modeling practices
• Communicate and collaborate with business stakeholders effectively to understand business needs and pain points to deliver high quality ML models that help propel business forward
• Collaborate with Model Risk Management team to demonstrate models are developed with high level rigor that satisfy Model Risk Management and Governance requirements
• Partner with the Product and Engineering teams for model deployment
• Perform ongoing monitoring of the models through the construction of dashboards and KPI tracking
• Present model performance and insights to Credit, Risk, and Business Unit leaders

• Bachelor's degree and a minimum of 12 years of proven quantitative behavioral modeling experience, or in lieu of a degree, a combined minimum of 12 years of higher education and / or work experience, including a minimum of 8 years of ML modeling experience. PHD preferred
• Excellent knowledge of machine learning and statistical modeling methods for supervised and unsupervised learning. These methods include (but not limited to) regression, clustering, outlier detection, novelty detection, decision trees, nearest neighbors, support vector machines, ensemble methods and boosting, neural networks, deep learning and its various applications. Continuously follow the advancement of machine learning and artificial intelligence to update your knowledge and skills in order to solve business problems with the most efficient methodologies
• Strong programming skills in Python
• Strong knowledge of databases and related languages / tools such as SQL
• Effective communication skills and ability to explain complex models in simple terms

• Experience in a financial organization
• Experience with model documentation and delivering effective verbal and written communication
• Experience in working closely with Product, Engineering, and Model Risk Management teams