Principal Modeler, Mortgage Loan Performance

About RiskSpan

We build the analytics and data infrastructure that mortgage and structured finance professionals rely on to understand risk, run models, and make decisions with confidence. Our platform, Edge, serves portfolio managers, risk teams, and quantitative analysts at some of the most sophisticated financial institutions in the market. We're growing and we need the operational foundation to match.

TL;DR

We're looking for a seasoned quantitative modeler to own the development and enhancement of loan-level mortgage prepayment and credit performance models. You'll bring deep domain expertise, set the technical standard for how we approach mortgage modeling, and partner directly with our structured finance and risk teams. This is a principal-level role for someone who has done this work before and is ready to own it.

What you'll do

Build

• Own and advance loan-level prepayment models across agency and non-agency collateral - S-curves, refinance incentive functions, seasoning ramps, burnout, seasonality, turnover etc
• Lead econometric and ML approaches for prepayment and credit behavior modeling, including survival analysis, competing risks, and gradient boosting; extend to default and severity modeling.
• Own credit risk modeling efforts including delinquency transitions, default, and loss given default
• Build full modeling pipelines in Python (pandas, NumPy, scikit-learn, statsmodels), R, and/or C++ on Linux - from data ingestion through validation and deployment

Validate and Research

• Back-test models and run sensitivity analysis across rate environments, vintages, and borrower cohorts
• Analyze GSE, GNMA, and private-label RMBS loan performance data using SQL and Snowflake to identify behavioral drivers and shifts
• Research macroeconomic and borrower-level prepayment drivers - mortgage rate spreads, home price appreciation, credit availability - and incorporate them into stochastic scenario design
• Apply Monte Carlo simulation, OAS frameworks, and interest rate models to support structured mortgage asset valuation and hedging

Integrate and document

• Partner with structured finance and risk teams to integrate models into pricing, OAS analysis, hedging, and risk management frameworks
• Set documentation standards and author technical model documentation and research notes for internal stakeholders, model risk management, and regulators
• Mentor and provide technical guidance to junior modelers on the team

Who you are

• Master's or Ph.D. in Quantitative Finance, Statistics, Econometrics, Applied Math, Physics, or a related field
• 7-10+ years of hands-on mortgage prepayment or credit performance modeling experience
• Deep expertise in agency and non-agency MBS markets, TBA pricing, prepayment benchmarks, and RMBS cash flow modeling
• Strong programming skills in Python, R, C++ on Unix/Linux, and SQL
• Experienced with statistical modeling - survival analysis, proportional hazard models, logistic regression, GLMs, panel data econometrics
• Proficient in analyzing large datasets using SQL, Snowflake, and cloud-based data environments
• Proven ability to set technical direction and drive long-term research projects through to deployment
• Exposure to Monte Carlo simulation, OAS, stress testing frameworks, or model governance a plus

What we offer

• Base salary range of $180,000 - $200,000

o Exact compensation depends on experience, skills, location, and market data

• Benefits package including paid time off, 401k, and medical, dental, and vision insurance options
• Meaningful work in a technically complex, high-stakes industry, building models that practitioners rely on
• A collaborative team that operates at the leading edge of mortgage and structured finance modeling