Senior Staff Data Scientist, Machine Learning

Sofi
San Francisco, CA

The role

As the Senior Staff Data Scientist Quantitative Credit Risk Management, Home Loans, you will be a key leader and expert in the development and management of quantitative credit risk models for our Mortgage, Home Equity, HELOC, and Jumbo loan products. This role is highly technical and requires a demonstrated ability to independently develop, implement, and maintain complex models used for underwriting and Risk Base Pricing and Portfolio Optimization. You will serve as an expert in key areas of quantitative risk management, providing leadership, guidance, and mentorship to less experienced analysts while collaborating with various business units and risk functions to ensure model accuracy and effective implementation. Your ability to communicate complex data and model results with clarity and precision to both technical and non-technical audiences is essential for success.

 

What you’ll do:



  • Model Development and Management: Independently develop, implement, maintain, and analyze quantitative/econometric credit risk models (e.g., loan delinquency, default, loss, prepayment, Risk Based Pricing, Credit Decision Models, and utilization models) specifically for home lending products.



  • Data Analysis: Prepare, manage, and analyze large customer loan, deposit, or financial data sets for statistical analysis to properly specify and estimate econometric models.



  • Technical Leadership: Serve as a quantitative expert in the use of statistical programming languages (SAS, Python, Stata, R) and data management environments, such as SQL Server Management Studio, to analyze Bank datasets.



  • Team Leadership: Provide mentoring, training, and guidance to less experienced analysts. Lead team-based projects related to model development or implementation, providing performance feedback to management as appropriate.



  • Cross-Functional Collaboration: Partner and collaborate with colleagues in Credit Risk Management, Business Units, Model Risk Management, Treasury and review functions (Credit Review, Audit, etc.) to implement and understand models.



  • Communication: Communicate with clear narratives, compelling data visualization, and technical precision to enable audiences to understand analysis and forecasts.



  • Model Documentation and Compliance: Develop, maintain, and manage satisfactory model documentation, including process narratives and performance monitoring guidelines. Understand and adhere to the Company's risk and regulatory standards, policies, and controls.



  • Performance Monitoring: Track portfolio performance, model performance, campaign tracking, and risk strategy results, incorporating new data and observations into existing models to improve predictive results.


 

What you’ll need:



  • 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 proven quantitative behavioral modeling experience.



  • Minimum of 8 years of on-the-job experience with pertinent statistical software packages (Python, Stata, R).



  • Minimum of 8 years of on-the-job experience with data management environments, such as SQL Server Management Studio.



  • Minimum of 8 years of on-the-job experience analyzing large data sets and explaining results of analysis through concise written and verbal communication as well as charts/graphs.



  • Credit modeling experience (mortgage and/or HELOC credit modeling) is required.



  • Proven ability to identify, analyze, rationalize, and communicate complex business, data, and statistical problems and recommend corresponding solutions.



  • Demonstrated attention to detail, execution, and follow-up on multiple initiatives.



  • Ability to work independently and manage complex projects.


 

Nice to have:



  • Experience in a fintech environment.



  • Experience with advanced data science platforms.


Posted 2025-10-01

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