Home Loans Loss Forecasting Analytics, Senior Data Scientist

SoFi is looking for a Senior Data Scientist to join its Secured Lending Team, focusing on Home Lending risk analytics, loss forecasting, and portfolio performance monitoring. The role will involve building models, dashboards, and analytical frameworks to understand credit performance across the residential lending lifecycle. The individual will partner with cross-functional stakeholders to develop roll-rate models, collections analytics, and portfolio risk insights.

Requirements

• 5+ years of experience in data science, statistical modeling, credit risk analytics, loss forecasting, portfolio analytics, or a related quantitative role.
• Master’s or PhD in Statistics, Mathematics, Economics, Engineering, Computer Science, Operations Research, Finance, or another quantitative field.
• 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.

Benefits

• Comprehensive and competitive benefits
• Equal employment opportunities
• Reasonable accommodations for candidates with physical or mental disabilities
• Flexible work arrangements

Tagged as: Forecasting