Full-Time
Principal Modeler
Mortgage Loan Performance
Updated on 9/3/2026
RiskSpan
51-200 employees
Data, models, and forecasts for mortgages
Compensation Overview
$180k - $200k/yr
Arlington County, Arlington, VA, USA
Hybrid
Three days on-site per week required.
Master's, PhD
 Quantitative Finance (1) |
|---|
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- A Master's or Ph.D. in Quantitative Finance, Statistics, Econometrics, Applied Mathematics, Physics, or a related field is required.
- At least 7-10 years of hands-on mortgage prepayment or credit performance modeling experience is required.
- Deep expertise in agency and non-agency mortgage-backed securities markets, TBA pricing, prepayment benchmarks, and residential mortgage-backed securities cash flow modeling is required.
- Strong programming skills in Python, R, C++, Unix/Linux, and SQL are required.
- Experience with statistical modeling methods including survival analysis, proportional hazard models, logistic regression, generalized linear models, and panel data econometrics is required.
- Proficiency analyzing large datasets using SQL, Snowflake, and cloud-based data environments is required.
- The candidate must be able to set technical direction and drive long-term research projects through deployment.
- Own and advance loan-level prepayment models across agency and non-agency collateral, including S-curves, refinance incentive functions, seasoning ramps, burnout, seasonality, and turnover.
- Lead econometric and machine learning approaches for prepayment and credit behavior modeling, including survival analysis, competing risks, and gradient boosting, and extend the work to default and severity modeling.
- Own credit risk modeling efforts including delinquency transitions, default, and loss given default.
- Build full modeling pipelines in Python using pandas, NumPy, scikit-learn, and statsmodels, as well as R and/or C++ on Linux, from data ingestion through validation and deployment.
- Back-test models and run sensitivity analyses across rate environments, vintages, and borrower cohorts.
- Analyze GSE, GNMA, and private-label residential mortgage-backed securities loan performance data using SQL and Snowflake to identify behavioral drivers and shifts.
- Research macroeconomic and borrower-level prepayment drivers, including mortgage rate spreads, home price appreciation, and credit availability, and incorporate them into stochastic scenario design.
- Apply Monte Carlo simulation, option-adjusted spread frameworks, and interest rate models to support structured mortgage asset valuation and hedging.
- Partner with structured finance and risk teams to integrate models into pricing, option-adjusted spread 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.
- Exposure to Monte Carlo simulation, option-adjusted spread frameworks, stress-testing frameworks, or model governance.
RiskSpan provides tools and services to manage risk for mortgage loans and structured products. Its Edge Platform offers data, predictive models, and scenario-based forecasts to analyze Agency and non-Agency MBS, loans, and MSRs. The platform uses cloud infrastructure, machine learning, and AI to scale model building and streamline information management, while its consultants design and develop custom solutions, automate workflows, and translate analytics into actionable insights. The company differentiates itself through a combination of a capable data-and-model platform and a team of experts who tailor solutions and workflows for capital markets, banking, and insurance clients. The goal is to help clients understand risk, optimize decisions, and make data-driven actions across their portfolios.
Company Size
51-200
Company Stage
N/A
Total Funding
N/A
Headquarters
Arlington, Virginia
Founded
2001
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Simplify's Take
What believers are saying
- July 2026 Credit Model 7.1 makes RiskSpan the only integrated NonQM prepay-credit vendor.
- NonQM issuance hit $20.9 billion in Q3 2025, expanding RiskSpan's addressable market.
- September 2025 Agentic AI for MBS Data broadens automation across loan tapes and workflows.
What critics are saying
- LSEG can absorb RiskSpan's pricing value proposition by bundling comparable analytics.
- NonQM delinquencies and weak vintages hit RiskSpan's core model credibility quickly.
- A single model miss during a 2026 NonQM downturn damages auditor and issuer trust.
What makes RiskSpan unique
- RiskSpan's July 2026 NonQM model segments bank-statement, DSCR, and full-doc loans separately.
- It trained on $87 billion UPB across 226,000 NonQM loans through August 2025.
- June 2026 LSEG partnership embeds RiskSpan into structured-finance evaluated pricing workflows.
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Benefits
Hybrid Work Options
Remote Work Options
Company News
RiskSpan launches Non-QM credit model trained on $87B in loans. Managing Editor Jul 22, 2026 Model separately analyzes bank-statement, DSCR, and full-documentation collateral using data from approximately 226,000 loans RiskSpan has launched a credit-risk model built specifically for non-qualified mortgages, giving investors and issuers a tool that accounts for differences among bank-statement, debt service coverage ratio, and full-documentation loans. Credit Model 7.1 is now generally available through the RiskSpan Platform. When paired with the company's existing Non-QM prepayment model, the new model allows users to conduct credit and prepayment analyses and generate loan-level cash flow projections within a single environment, according to RiskSpan. Morningstar DBRS reported that Non-QM residential mortgage-backed securities issuance reached a record $20.9 billion during the third quarter of 2025, up 97% from $10.6 billion one year earlier, according to figures cited by RiskSpan. Fitch Ratings reported that issuance within its rated Non-QM and non-prime RMBS portfolio increased by more than 800% between 2020 and 2023. KBRA, meanwhile, projects that overall non-agency RMBS issuance - a broader category that includes Non-QM securities - will increase 15% in 2026 to $160 billion. That growth has increased the volume of loan-level data investors and issuers must evaluate when pricing transactions and making allocation decisions. RiskSpan contends that models developed for agency mortgages or older non-agency collateral do not fully capture the borrower behavior found across different Non-QM documentation types. Get the NMP Daily Essential stories, every weekday. Model separates collateral by documentation type. Credit Model 7.1 uses a transition-state framework, with loan-performance transitions estimated independently for bank-statement, DSCR, full-documentation, and other loan categories. That segmentation is intended to capture the differences in borrower behavior and credit performance among products that may all fall under the broader Non-QM label. "Non-QM borrower behavior varies meaningfully by documentation type, and generic credit and prepay frameworks simply don't capture that," said Divas Sanwal, head of modeling at RiskSpan. "Credit Model 7.1 was built from the ground up on Non-QM collateral, segmented by doc type, and validated with published backtesting - giving risk teams, auditors, and counterparties the transparency they need to stand behind the model." The model incorporates 10 loan- and borrower-level variables, including credit scores, mark-to-market loan-to-value ratios, debt-to-income ratios, and loan purpose. It also incorporates three macroeconomic drivers. RiskSpan trained the model on approximately $87 billion in unpaid principal balance across roughly 226,000 Non-QM loans originated or outstanding between January 2018 and August 2025. The distinction among loan types has become more consequential as Non-QM volume increases. NMP previously reported that delinquencies rose across the Non-QM and non-prime RMBS sector, with Fitch identifying deterioration among newer loan vintages. Tape analysis and API access included. The release also includes artificial intelligence-powered tools for processing loan tapes and analyzing collateral. Clients can access model results through an application programming interface and integrate the output into their existing systems. RiskSpan said it plans to add a user-facing backtesting dashboard. Container deployment and additional integrations are also planned for later phases. Credit Model 7.1 is currently available to RiskSpan Platform and Loans Module clients. RiskSpan's model reflects a more granular approach to Non-QM risk: bank-statement, DSCR, full-documentation, and other loans are modeled separately rather than treated as a single collateral category. For mortgage bankers, that distinction could help identify which documentation types are driving expected losses or prepayments within a loan pool, informing aggregation, securitization, and capital allocation decisions. *This article was primarily written by a human author. AI tools were used in a limited capacity for research assistance or light editing. Managing Editor Czarinna Andres leads editorial coverage for NMP, focusing on the trends, policies, and business strategies shaping today's mortgage and housing finance landscape. She brings a background in journalism and media, with experience... Jul 22, 2026
RiskSpan has launched Credit Model 7.1, a purpose-built NonQM credit model within its platform. The model addresses the surging NonQM residential mortgage-backed securities market, which saw issuance nearly double year-over-year to $20.9 billion in Q3 2025, according to Morningstar DBRS. Credit Model 7.1 features transition-state modelling for NonQM collateral segmented by documentation types including Bank Statement, DSCR, and Full Doc. The model was trained on approximately $87 billion in unpaid principal balance across roughly 226,000 NonQM loans from January 2018 through August 2025. Combined with RiskSpan's existing NonQM prepayment model, the company now offers the only vendor solution providing both purpose-built prepay and credit models for NonQM alongside integrated workflow tools in a single environment.
The insurance ABF stack: panel takeaways. Riskspan Inc opened the insurance panel at the RiskSpan Summit earlier this month with an interesting statistic: according to Moody's, almost a third of the $6 trillion in cash and invested assets held by US life insurers is now allocated across private credit sub-asset classes. Nancy Mueller Handal of Bayview confirmed it tracks with consideration to private placements, commercial mortgages, and infrastructure debt alongside direct lending. Bill Moretti of Equitable added a useful caveat: the industry still doesn't have a settled definition of "private credit," and ABF has only recently been pulled under that umbrella. However you count it, the scale is real and it's happened fast. I was joined by Nancy, Bill, and Larry Yang of Global Atlantic / KKR - three practitioners operating through fundamentally different models. What made the conversation worth having was exactly that diversity of structure. Here's what stuck with me. You may not know you're in a bad vintage while you're living through it. This was the sharpest exchange of the panel. The group flagged the current non-QM environment specifically: massive appetite, compressed spreads, and layered risk that has never been tested in a declining home price environment. Non-QM has existed entirely within a rising housing market. There was even speculation that Riskspan Inc may be experiencing a vintage risk event in data centers right now - and the industry won't know for years. The most uncomfortable version of vintage risk isn't the one you can see. It's the one you're inside. The data problem is harder than the analytics problem. One panelist observed that in structured finance, "you could get to a point where you don't know what you own." Larry described building proprietary end-to-end infrastructure - including custom waterfall models - because off-the-shelf systems aren't granular enough. Nancy's observation was the most memorable: her research team has grown as capabilities have improved, not shrunk. More tools enable more questions. The data was always there. The limiting factor has always been the ability to extract, manage, and act on it in real time. AI is a co-pilot, not a substitute. The panel's most grounded take: "Seasoned people know what questions to ask - and know when the answer is wrong." Junior analysts prompting a model without that judgment aren't getting the same output. The firms making real progress are the ones embedding AI into durable processes, not just re-prompting the same task each month. And on why they're still leaning in: Nancy called it "the most exciting market out there." Larry cited the breadth: in ABF, you're always encountering asset classes you've never seen before. The undisputed line of the day: "Insurance companies are now sexy." Hard to argue with that. The RiskSpan Summit 2026 brought together practitioners across insurance, asset management, and structured finance.
This month, RiskSpan Inc. showcased its responsiveness to shifting macroeconomic dynamics and introduced new transparency elements (i.e., back-testing tools) to its prepayment and credit modeling.
As a forward-looking initiative, RiskSpan Inc. is developing a generalized spread model that isolates residual pricing differences not explained by known borrower or loan characteristics.