Home Loans Loss Forecasting Analytics, Senior Data Scientist

Employee Applicant Privacy Notice 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 We are looking for a Senior Data Scientist to join SoFi’s Secured Lending Team, with a focus on Home Lending risk analytics, loss forecasting, and portfolio performance monitoring. This role will support home lending products including first mortgages, jumbo loans, closed-end seconds, and HELOCs, with a strong emphasis on delinquency, default, cure, severity, recovery, and portfolio profitability. The Senior Data Scientist will play a key role in building models, dashboards, and analytical frameworks that help the Secured Lending organization understand credit performance across the full residential lending lifecycle — from origination and portfolio monitoring through delinquency, default resolution, loss mitigation, and recovery. This individual will partner closely with Credit Decision Science, Credit Risk, Finance, Capital Markets, Servicing, Collections, Loss Mitigation, Model Risk, and Data Engineering to support data-driven decision-making across Home Lending. 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 company with the stability of an industry-leading leadership team. What you’ll do The Senior Data Scientist will help SoFi strengthen Home Lending risk analytics, forecasting, and portfolio management by: Developing quantitative and machine learning models to forecast losses across mortgage and home equity portfolios, including first lien, jumbo, HELOC, and closed-end second-lien products. Building and maintaining CECL, loss forecasting, and portfolio performance models with a focus on delinquency roll rates, default probability, cure behavior, loss severity, recovery timing, prepayment behavior, and charge-off outcomes. Defining and maintaining portfolio performance KPIs across credit, profitability, and risk, including delinquency rates, roll rates, cure rates, loss rates, severity, prepayment speeds, early payment defaults, repurchase risk, defect rates, and recovery performance. Performing cohort, vintage, and segmentation analysis by credit score, LTV/CLTV, DTI, lien position, documentation type, occupancy, channel, state/metro, property type, investor, and product type. Analyzing borrower behavior and identifying key risk drivers across stages of credit performance, including current status, early delinquency, late-stage delinquency, default, liquidation, foreclosure, recovery, and redefault. Building roll-rate models, delinquency migration analytics, cure models, default models, recovery models, and loss severity frameworks for secured lending portfolios. Supporting collections, loss mitigation, and default strategy analytics, including segmentation, treatment strategy measurement, liquidation waterfalls, cure versus liquidation outcomes, modification performance, and recovery optimization. Developing analytics that evaluate resolution pathways, including cure, modification, repayment plan, foreclosure, liquidation, REO, charge-off, and expected recovery cash flows. Building and maintaining executive dashboards and automated reporting that clearly explain what changed, why it changed, and what actions should be considered next. Partnering with Data Engineering to define data requirements, improve data quality, create new data sources, and build summarized analytical tables that support scalable reporting, monitoring, and modeling. Aggregating and synthesizing datasets from multiple environments, including origination data, servicing systems, collections data, collateral data, bureau data, investor/product data, and external housing market data such as HPI. Performing sensitivity, scenario, and stress analysis tied to home price movements, interest rates, unemployment, credit mix, prepayment behavior, and broader economic conditions. Monitoring model and portfolio performance through back-testing, forecast-to-actual tracking, population stability, segmentation diagnostics, drift monitoring, and periodic recalibration. Preparing clear, audit-ready documentation for models, assumptions, dashboards, data sources, business logic, reporting definitions, and governance routines. Partnering with Credit Decision Science and other cross-functional stakeholders to d