Title: Mortgage/Data Analyst
Compensation: 100K-135K base
No 3rd party/C2C- No sponsorship at this time
Client is re-writing the risk model for the non-QM mortgage market -combining data analytics, proprietary risk intelligence, and Loan Defect Insurance to turn mortgage manufacturing risk into quantifiable, insurable outcomes for lenders, investors, and RMBS issuers. Client AI (SAI) is the analytical engine powering this platform, built on neural network triage, hazard-based pricing, and AI-driven defect detection. The Mortgage / Data Analyst is the person who makes SAI's outputs defensible to the outside world -bridging model outputs and real-world loan performance through surveillance reporting, model validation, and institutional counterparty deliverables.
In this role you will:
· Produce surveillance reporting -pool health summaries, delinquency trends, roll rate analysis, and defect emergence narratives supporting institutional counterparty relationships
· Maintain mark-to-market LTV calculations using live HPI feeds and produce early-warning indicators for pools approaching concentration thresholds or deteriorating collateral values
· Own quarterly reinsurer reporting -claims bordereau data, aggregate exposure summaries, and pool performance vs. pricing assumption variance
· Build and maintain investor report templates translating model outputs into plain-language risk narratives for capital markets counterparties
· Perform loan-level file review and model output validation -comparing defect flags and risk scores against loan documents to identify systematic errors or coverage gaps
· Contribute realized loss experience and recovery timing data that calibrates SAI's claims analytics models
The Ideal Candidate:
· Has sat in a loan file and knows what makes a defect real vs. a model flag
· Can translate complex model output into language a reinsurer's credit committee will act on
· Brings equal comfort to Excel and SQL
Basic Qualifications:
· 4+ years in non-QM loan review, mortgage credit risk, or structured finance analytics
· Deep familiarity with non-QM loan types: DSCR, bank statement, asset depletion, foreign national
· SQL and Excel or Python for analysis and reporting
· Performance reports and risk narratives produced for institutional audiences
Preferred Qualifications:
· Quantitative model outputs translated into investor or counterparty-facing narratives
· RMBS or whole loan pool-level surveillance reporting
· Mark-to-market LTV monitoring using CoreLogic, FHFA, or equivalent HPI data
Tech: SQL · Python or R · Excel · Tableau or Power BI · HPI data feeds (CoreLogic / FHFA)