Home Loans Loss Forecasting Analytics, Senior Data Scientist

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Who we are:

The role

We are looking for a Senior Data Scientist to join SoFis 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 youll 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 youll 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 develop roll-rate models collections analytics loss forecasting enhancements and portfolio risk insights.
• Translating complex analysis into concise executive-ready recommendations for Credit Risk Finance Capital Markets Accounting Model Risk and Secured Lending leadership.

What youll need

• 5 years of experience in data science statistical modeling credit risk analytics loss forecasting portfolio analytics or a related quantitative role.
• Masters or PhD in Statistics Mathematics Economics Engineering Computer Science Operations Research Finance or another quantitative field; equivalent practical experience will also be considered.
• Strong proficiency in Python and SQL with experience building repeatable analytical pipelines model monitoring routines and automated reporting.
• Experience with data visualization and dashboarding tools such as Tableau Looker Power BI or similar platforms.
• Demonstrated experience with credit risk modeling loss forecasting CECL roll-rate modeling delinquency/default modeling recovery modeling or portfolio performance analytics.
• Hands-on experience with mortgage or secured lending data including first liens jumbo loans HELOCs closed-end seconds or other collateral-backed products.
• Strong understanding of mortgage credit risk drivers including FICO LTV/CLTV DTI lien position occupancy documentation type channel geography property type investor/product collateral value and HPI.
• Experience analyzing delinquent non-performing or defaulted loan portfolios including roll rates cure rates charge-offs recoveries redefault behavior and severity.
• Familiarity with statistical and machine learning methods such as regression survival analysis time-series modeling Markov/state transition models gradient boosting random forests clustering and model calibration.
• Strong analytical communication skills with the ability to explain model outputs portfolio trends and risk drivers to both technical and non-technical audiences.
• Ability to operate in a governed risk management environment with attention to auditability documentation controls and model risk expectations.