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Credit Risk Model Validation Quantitative Analyst Jobs

... Finance, Quantitative Research, and Growth Analytics to remediate validation findings and ... Experience in credit underwriting, credit risk management is a plus * Proven ability to work with ...

Experience reviewing or validating quantitative and/or qualitative models, such as credit risk, allowance, forecasting, pricing, BSA/AML, or operational models. * Ability to analyze model ...

... Model Validation, Quantitative Risk, or Enterprise Risk Management within banking or financial ... Excellent analytical, documentation, communication, and presentation skills. Preferred ...

Experience reviewing or validating quantitative and/or qualitative models, such as credit risk, allowance, forecasting, pricing, BSA/AML, or operational models. * Ability to analyze model ...

Experience reviewing or validating quantitative and/or qualitative models, such as credit risk, allowance, forecasting, pricing, BSA/AML, or operational models. * Ability to analyze model ...

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Credit Risk Model Validation Quantitative Analyst information

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$37K

$113.9K

$197.5K

How much do credit risk model validation quantitative analyst jobs pay per year?

As of Sep 11, 2026, the average yearly pay for credit risk model validation quantitative analyst in the United States is $113,881.00, according to ZipRecruiter salary data. Most workers in this role earn between $82,500.00 and $140,500.00 per year, depending on experience, location, and employer.

What is a credit risk model validation quantitative analyst?

A Credit Risk Model Validation Quantitative Analyst is a finance professional responsible for assessing and validating the accuracy, reliability, and performance of credit risk models used by financial institutions. Their work involves reviewing statistical methods, testing model assumptions, and ensuring compliance with regulatory requirements. By identifying model weaknesses and recommending improvements, they help institutions manage risk effectively and maintain sound lending practices.

What are the key skills and qualifications needed to thrive as a credit risk model validation quantitative analyst?

To thrive as a Credit Risk Model Validation Quantitative Analyst, you need a solid background in quantitative finance, statistics, and programming, often supported by an advanced degree in a quantitative field. Proficiency with statistical software such as Python, R, SAS, and familiarity with regulatory frameworks like SR 11-7 and Basel guidelines are typically required. Strong analytical thinking, attention to detail, and effective communication skills help you interpret model results and convey complex findings to stakeholders. These competencies ensure models are robust, compliant, and transparent, safeguarding the organization's financial stability and regulatory standing.

What are the typical challenges faced by a credit risk model validation quantitative analyst when reviewing complex financial models?

Credit Risk Model Validation Quantitative Analysts often encounter challenges such as interpreting complex model methodologies, ensuring data integrity, and identifying model limitations or weaknesses. They must thoroughly assess both conceptual soundness and technical implementation, which can involve dissecting highly sophisticated quantitative techniques and large datasets. Collaboration with model developers and business stakeholders is essential to address findings, communicate technical issues clearly, and ensure regulatory compliance. Staying current with evolving regulatory standards and best practices is also a key aspect of the role.

What are popular job titles related to Credit Risk Model Validation Quantitative Analyst jobs?

For Credit Risk Model Validation Quantitative Analyst jobs, the most frequently searched job titles are:

Infographic showing various Credit Risk Model Validation Quantitative Analyst job openings in the United States as of September 2026, with employment types broken down into 2% As Needed, 83% Full Time, 13% Part Time, and 2% Contract. Highlights an 86% Physical, 3% Hybrid, and 11% Remote job distribution, with an average salary of $113,881 per year, or $54.8 per hour.

Model Risk Analyst - Validation [Multiple Positions Available]

Buffalo, NY • On-site

M&T Bank
Finance and Insurance • 10K+ employees

$119K/yr

Full-time

Re-posted 14 hours ago


M&T Bank rating

7.9

Company rating: 7.9 out of 10

Based on 188 frontline employees who took The Breakroom Quiz


Job description

Title: Model Risk Analyst - Validation [Multiple Positions Available]
Job Location: 345 Main St, Buffalo, NY 14203. Position requires in-office work four (4) days every week.
Job Description: Perform independent validation review of complex financial statistical models with primary focus on Treasury models (including interest rates sensitive, interest rates and currency derivatives models), as well as Fair Lending and general Credit Risk models. Writing quality reports for management review. Preparing materials and leading Effective Challenge presentations of accomplished validations. Supporting MRM model life cycle in relations to reviewing and reporting of IRA, MCA, MCM, MRT. Establishing communication with model owners, model developers, model stakeholders in support of successful model validation process and MRM initiatives. Supporting development of playbook for Validation of Fair Lending Models. Coordinate the engagement of third parties to perform validation. Review the results of third party validation. Perform validation and analysis of expert judgment or qualitative factors that augment quantitative models. Review to confirm proper controls and adequate documentation are in place. Recommend, as necessary, the cessation of reliance on models that are outdated or inaccurate, as determined by analysis. Prepare reporting for Management to monitor performance of models. Participate in meetings with model owners to discuss current portfolio tracking and business observations. Develop knowledge on standard concepts, practices, and procedures within the model validation/risk analytics field. Mine data from a variety of sources. Utilize technical skills to manage data and efficiently conduct analyses. Develop ad hoc processes to address efficiency gains that translate into repeatable procedures. Prepare written summary and analysis of all validation work, using a combination of word processing and presentation software skills. Adhere to applicable compliance/operational risk controls in accordance with Company or regulatory standards and policies. Maintain M&T internal control standards, including timely implementation of internal and external audit points together with any issues raised by external regulators as applicable.
Minimum requirements: Master's degree (or foreign equivalent) in Financial Mathematics, Mathematics, Statistics, Computer Science, Operation Research, Econometrics, or a related technical field plus three (3) years of experience in the job offered or as a Model Risk Analyst, Quantitative Model Developer, Quantitative Financial Analyst, Statistician, Data Scientist, or Model Validator. The employer will alternatively accept a Bachelor's degree (or foreign equivalent) in Financial Mathematics, Mathematics, Statistics, Computer Science, Operation Research, Econometrics, or related technical field plus six (6) years of experience in the job offered or as Quantitative Model Developer, Quantitative Financial Analyst, Statistician, Data Scientist, or Model Validator.
Requires three (3) years of experience in each of the following:
• Statistical modeling techniques including regression (including linear, logistic, Poisson, lasso, and ridge), machine learning (including tree and XGBoost), and cluster analysis.
• Programming skills in Python or SAS.
• Work with supervised models (including regressions, boosting, and ensemble learning) and unsupervised algorithms (including clustering and DBSCAN) applied to quantitative risk modeling and data-driven analysis.
• Statistical theory, including sampling methods, confidence intervals, and hypothesis testing for evaluating model assumptions and performance.
• Programming languages including Python or SAS for statistical modeling, machine learning development, implementation, future engineering and model performance evaluation.
• Writing reproducible code.
• Data wrangling, automation, and generating analytical reports.
• Leveraging SQL and other query languages to query, transform, and preprocess structured and unstructured data for analytical and modeling purposes.
• Working with data mining and feature engineering techniques.
Salary: $119,766.00 - $119,766.00 per year
Location
Buffalo, New York, United States of America

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