Overview

Senior Quantitative Analyst Jobs in Greater Chicago Area at Euclid Mortgage Insurance Services, LLC

Title: Senior Quantitative Analyst

Company: Euclid Mortgage Insurance Services, LLC

Location: Greater Chicago Area

We are seeking a Senior Quantitative Analyst — Mortgage Credit Risk to serve as the core technical modeler at our (MGA). Our firm underwrites mortgage credit risk sourced primarily from private mortgage insurers and the government-sponsored enterprises (GSEs). This is a high-impact, hands-on role for a technically skilled professional who thrives at the intersection of mortgage finance, data engineering, and predictive analytics.

The Senior Quantitative Analyst will be responsible for the design, development, and production deployment of credit risk models that drive our underwriting, pricing, portfolio monitoring, and stress-testing capabilities. You will work directly with large-scale loan performance datasets from Fannie Mae and Freddie Mac, integrate macroeconomic scenario data from Moody’s Analytics Data Buffet, and operate within a modern Microsoft Fabric analytics environment.

This role bridges the gap between technical modeling and insurance financial analysis. You will be responsible for translating credit risk model outputs into actionable insurance and financial indications — including written premium, earned premium, loss development, loss ratios, combined ratios, probable maximum loss (PML) estimates, and return-on-capital metrics — that directly inform pricing, reserving, and capital allocation decisions.

This role is ideal for a quantitative professional with 3–5 years of experience in mortgage credit risk modeling — or an actuarial professional pivoting from mortgage insurance, P&C reinsurance, or structured credit — who is ready to own the analytical engine of a nimble, growing organization. You will work closely with senior leadership and have meaningful influence over the firm’s risk strategy. Given our lean team structure, we also value candidates who leverage AI-assisted coding and prompting tools to maximize productivity and accelerate development cycles.

KEY RESPONSIBILITIES

Model Development & Maintenance

  • Design, build, validate, and maintain credit risk models including probability of default (PD), loss given default (LGD), prepayment (CPR/CDR), and severity models for residential mortgage portfolios
  • Develop and refine regression-based models (logistic, Cox proportional hazard, competing-risk) and time-series forecasting frameworks for delinquency transition, default, and loss projection
  • Utilize DataRobot’s automated machine learning (AutoML) platform to accelerate model selection, feature engineering, and algorithm benchmarking — rapidly evaluating candidate models across large loan performance datasets and comparing AutoML outputs against hand-built specifications to identify optimal approaches for each risk dimension
  • Construct scenario-based stress-testing models that incorporate macroeconomic drivers such as unemployment, HPA, and interest rate paths sourced from Moody’s Analytics Data Buffet, Federal Reserve Economic Data (FRED) and other data providers
  • Perform ongoing model performance monitoring, backtesting, and recalibration to ensure model accuracy and regulatory defensibility

Data Engineering & Platform Management

  • Build and maintain ETL pipelines within Microsoft Fabric to ingest, transform, and curate large loan-level performance datasets from Fannie Mae and Freddie Mac (Single-Family Loan Performance Data)
  • Build and maintain API integrations with Moody’s Data Buffet and other third-party economic data providers to automate scenario and variable ingestion
  • Support the design and optimization of the Snowflake, Fabric Lakehouse and warehouse layers for analytical workloads, ensuring data quality, lineage, and governance standards are met
  • Develop reproducible, version-controlled modeling workflows using Python, PySpark, and/or R within Fabric notebooks

Analytics, Reporting & Strategy

  • Translate model outputs into actionable underwriting guidelines, pricing indications, and portfolio risk metrics for senior leadership and reinsurance/capital partners
  • Develop and maintain insurance financial projections including written premium, earned premium, incurred loss, loss development triangles, loss ratios, combined ratios, probable maximum loss (PML) estimates, and return-on-capital analyses
  • Build interactive dashboards and automated reporting using Power BI integrated with Microsoft Fabric for ongoing portfolio surveillance, reserving support, and capital adequacy monitoring
  • Support new business evaluation by modeling expected loss and return profiles on prospective mortgage insurance and credit risk transfer (CRT) opportunities
  • Monitor industry trends in GSE credit risk transfer programs (CAS, STACR, CIRT) and private MI performance to inform strategic positioning

AI-Augmented Development & Productivity

  • Leverage AI-assisted coding tools (e.g., GitHub Copilot, Claude, ChatGPT) and prompt engineering techniques to accelerate model prototyping, code generation, documentation, and data exploration
  • Evaluate and integrate emerging AI/ML capabilities into the modeling workflow where they add rigor or efficiency, including natural language interfaces for data querying and automated report generation
  • Actively identify opportunities to use AI-augmented workflows to multiply individual output and offset lean staffing

Collaboration & Process Development

  • Partner with underwriting, business development, and executive teams to embed model insights and insurance financial indications into operational decision-making
  • Contribute to the development of model documentation, validation procedures, and governance frameworks appropriate for an MGA environment
  • Support senior leadership in responding to technical and quantitative inquiries from rating agencies, reinsurers, and regulatory stakeholders

DESIRED QUALIFICATIONS

Education

Master’s degree in a quantitative discipline such as statistics, mathematics, actuarial science, economics, financial engineering, data science, or a related field. Candidates holding or pursuing actuarial credentials (ASA, ACAS, FSA, FCAS) are strongly encouraged to apply. A bachelor’s degree with exceptionally strong applied experience will also be considered.

Experience

3–5 years of experience in quantitative modeling with direct exposure to residential mortgage credit risk. Prior experience at a mortgage insurer, GSE, monoline, credit risk transfer desk, reinsurer, or mortgage-focused analytics firm is strongly preferred. Candidates with an actuarial background in mortgage insurance, mortgage guaranty, or P&C reinsurance who are seeking a more technically hands-on role are especially well-suited.

Technical Skills

  • Proficiency in Python (pandas, scikit-learn, statsmodels, PySpark) and/or R for statistical modeling and data manipulation
  • Hands-on experience with advanced regression techniques (logistic, survival/hazard, panel data), time-series methods (ARIMA, VAR, state-space), and machine learning approaches for credit risk
  • Experience with or interest in AutoML platforms, particularly DataRobot, for automated model selection, feature discovery, and performance benchmarking against traditionally specified models
  • Experience with or exposure to Microsoft Fabric (or equivalent modern cloud analytics platforms such as Databricks, Azure Synapse, or Snowflake), including Lakehouse architecture, notebooks, and data pipelines
  • Experience building and consuming RESTful APIs for data integration, particularly with economic or financial data providers
  • Competence with ETL design patterns, data warehousing concepts, and SQL for large-scale data operations
  • Familiarity with Power BI or similar visualization tools for model output reporting and executive dashboards
  • Demonstrated proficiency with AI-assisted development tools and large language model prompting (e.g., Claude, ChatGPT, GitHub Copilot) for code generation, data analysis, documentation, and workflow automation

Domain Knowledge

  • Solid understanding of residential mortgage credit fundamentals: underwriting variables (LTV, FICO, DTI), loan performance dynamics, and loss mitigation
  • Familiarity with GSE loan-level datasets (Fannie Mae/Freddie Mac Single-Family Loan Performance Data), their structures, and known data nuances
  • Understanding of private mortgage insurance economics, GSE credit risk transfer programs, and the MGA/reinsurance ecosystem
  • Exposure to insurance financial concepts including written and earned premium, loss development and reserving methods, loss ratios, combined ratios, probable maximum loss (PML), and return-on-capital frameworks (willingness to develop deep competency is essential)
  • Awareness of relevant regulatory frameworks (PMIERs, GSE seller/servicer guides, state insurance regulations) as they relate to model governance

Soft Skills

  • Ability to communicate complex quantitative concepts to non-technical stakeholders and senior executives
  • Self-directed, resourceful, and comfortable operating with a high degree of autonomy in a lean organization
  • Strong intellectual curiosity and a commitment to continuous improvement in both methodology and tooling, with an AI-forward mindset toward maximizing individual and team productivity

Interested candidates should submit a resume and a brief cover letter describing their experience with mortgage credit risk modeling and their interest in contributing to an early-stage MGA environment. We value practical demonstration of skill — candidates are encouraged to reference specific modeling projects, published research, or portfolio analytics work in their application.

PLEASE APPLY AT: https://euclidmortgage.com/careers/

Euclid Insurance Services Inc. and Euclid Mortgage Insurance Services, LLC are equal opportunity employers who recruit, employ, train, compensate and promote regardless of race, religion, color, national origin, gender, gender identity, sexual or affectional orientation, disability, age, veteran status, and other protected status as required by applicable law.

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