Senior Quantitative Modeler

Guidehouse•Tysons Corner, VA

1d•$130,000 - $216,000

About The Position

Guidehouse is seeking an experienced Senior Quantitative Modeler to support complex modeling, research, and analytical initiatives for federal financial services clients. The role combines statistical and econometric modeling expertise with strong SAS programming skills and experience working with large financial datasets and models in controlled analytical environments. The modeler independently develops, executes, maintains, troubleshoots, and enhances quantitative models, collaborating with economists, financial analysts, data specialists, technology teams, and client stakeholders. The position requires translating economic and financial concepts into sound quantitative methodologies and clearly communicating results to technical and executive audiences. The role involves developing and maintaining statistical, econometric, financial, and cash-flow models for complex financial assets and portfolios; applying statistical and econometric methods; managing and validating large datasets; running models and scenario analyses; and investigating anomalous results. It also supports model documentation, testing, quality control, change management, reproducibility, audit readiness, and preparation of analytical deliverables. Workloads may be executed in cloud environments in collaboration with technology teams. Candidates must be U.S. citizens residing in the United States and must obtain approved Federal or DoD Public Trust adjudication before onboarding, with the ability to maintain the trust. The role requires a bachelor's degree in a quantitative field and five to seven years of relevant experience. The annual salary range is $130,000 to $216,000, with compensation determined by factors including skills, experience, training, clearances, licensure, certifications, and organizational needs.

Requirements

Must be able to obtain and maintain a Federal or DoD Public Trust; approved adjudication is required before onboarding.
Must be a U.S. citizen and reside within the United States.
Bachelor's degree in economics, finance, statistics, mathematics, data science, computer science, engineering, or another quantitative field.
Five to seven years of relevant experience in quantitative modeling, econometrics, financial analytics, or statistical programming.
Strong hands-on SAS programming experience, including developing, understanding, maintaining, and troubleshooting existing SAS code and analytical workflows.
Demonstrated experience developing, executing, or enhancing statistical, econometric, financial, or cash-flow models involving complex financial assets or portfolios.
Working knowledge of statistical and econometric methodologies such as regression, logistic regression, survival analysis, time-series analysis, or comparable techniques.
Experience working with large and complex datasets, including data preparation, transformation, reconciliation, validation, and quality control.
Experience with an additional analytical or programming language, preferably Python or R.
Ability to independently investigate and resolve modeling issues involving data, assumptions, methodology, code, and analytical results.
Experience preparing technical and model documentation and communicating quantitative findings to technical and nontechnical audiences.
Strong written and verbal communication skills and ability to work effectively across multidisciplinary teams.

Nice To Haves

Experience modeling mortgage, mortgage-backed securities (MBS), consumer credit, fixed-income, or other large financial-asset portfolios.
Knowledge of mortgage performance concepts, including origination, delinquency, default, prepayment, buyout, foreclosure, and cash-flow behavior.
Familiarity with the U.S. housing finance and secondary mortgage markets, including Ginnie Mae, Fannie Mae, Freddie Mac, FHA, VA, or USDA programs.
Experience supporting quantitative models in federal government, regulated financial services, or other security-sensitive environments.
Experience with model governance, testing, validation, controlled releases, configuration and change management, and audit-ready documentation.
Experience using AWS services or capabilities to support data processing, analytical, or modeling workloads.
CFA, FRM, or another relevant quantitative or financial professional credential.

Responsibilities

Develop, maintain, and enhance statistical, econometric, financial, and cash-flow models used to analyze complex financial assets and portfolios.
Apply statistical and econometric techniques, including ordinary least squares, logistic regression, survival analysis, time-series analysis, and other predictive or behavioral modeling methods.
Develop, review, troubleshoot, and maintain SAS code supporting production and research modeling processes.
Collect, clean, transform, reconcile, validate, and integrate large datasets from multiple sources, assessing data quality, consistency, and completeness.
Conduct model runs, scenario and sensitivity analyses, and quantitative research; investigate anomalous results and resolve issues related to data, assumptions, methodology, code, or system configuration.
Apply economic theory, financial-market concepts, academic research, and empirical analysis to model development and enhancement.
Execute analytical and modeling workloads in cloud environments and collaborate with technology teams supporting the underlying infrastructure.
Develop and maintain documentation covering methodologies, assumptions, model inputs, calculations, source data, code, controls, outputs, and analytical conclusions.
Support model testing, quality control, change management, reproducibility, and audit-readiness activities.
Prepare analytical deliverables, including tables, charts, research memoranda, technical documentation, presentations, and executive-level summaries.
Present modeling approaches, findings, and technical issues clearly to stakeholders with varying levels of quantitative expertise.
Collaborate across multidisciplinary teams while managing multiple analytical priorities and deadlines.