Quantitative Model Risk Analyst

Rekruitd; Chicago (IL); On-site; USD 90,000 - 120,000; Full time

Job summary

Rekruitd is seeking a quantitative validation professional to review and challenge models used across banking and risk functions. You will assess design, data, and performance, and communicate risks and improvements clearly.

The role requires 1–3 years in quantitative analysis or validation, with Python experience and exposure to financial services models. Strong communication is essential, and ML/AI exposure is a plus.

Qualifications

• 1–3 years of experience in quantitative analysis or model validation.
• Exposure to financial services models (credit risk, CECL, liquidity, BSA/AML, or fair lending).
• Master's degree or PhD in a quantitative field.
• Hands-on Python for data analysis and testing.
• Strong analytical and written communication skills.
• Exposure to machine learning or AI modeling is a plus.

Responsibilities

• Review quantitative models and analytical methodologies used across banking and risk functions.
• Assess model design, assumptions, data, calculations, implementation, controls, and ongoing performance.
• Use independent analysis to challenge results and determine whether methodologies are appropriate for their intended use.
• Perform quantitative testing and develop comparison approaches to evaluate model accuracy and stability.
• Review models supporting lending, credit risk, regulatory compliance, financial crime, and valuation.
• Evaluate traditional statistical models as well as newer ML/AI approaches.
• Document your analysis and clearly communicate conclusions, risks, and recommended improvements.
• Partner with model developers, business teams, and risk stakeholders to understand methodologies and resolve findings.
• Contribute to the broader model governance program, including reporting and regulatory support.
• Look for ways to make validation and review processes more efficient through improved tools and automation.

Skills

Python

Statistics

Quantitative analysis

Data science

Model validation

Communication

Education

Master's degree in Mathematics/Statistics/Economics or related

Tools

R

SQL

Pandas

Job description

This role is part of an enterprise risk function responsible for providing independent oversight of models used to support business, risk, compliance, and financial decisions. We are looking for someone with a strong quantitative foundation who can analyze how models are designed, evaluate whether they are performing as intended, and clearly communicate potential risks or areas for improvement.

• Review quantitative models and analytical methodologies used across a variety of banking and risk functions.
• Assess model design, assumptions, underlying data, calculations, implementation, controls, and ongoing performance.
• Use independent analysis to challenge model results and determine whether methodologies are appropriate for their intended use.
• Perform quantitative testing and develop comparison approaches to evaluate model accuracy and stability.
• Review models supporting areas such as lending and credit risk, financial forecasting, regulatory compliance, financial crime, and valuation.
• Evaluate traditional statistical models as well as newer machine learning and AI-based approaches.
• Document your analysis and clearly communicate conclusions, identified risks, and recommended improvements.
• Partner with model developers, business teams, and risk stakeholders to understand methodologies and resolve findings.
• Contribute to the broader model governance program, including reporting and regulatory support.
• Look for ways to make validation and review processes more efficient through improved tools and automation.

Experience:

• 1–3 years of experience in quantitative analysis, model validation, model development, data science, or a similar area.
• Exposure to financial services models, particularly within areas such as credit risk, CECL, liquidity, compliance, BSA/AML, or fair lending.
• A Master's degree or PhD in Mathematics, Statistics, Economics, or another highly quantitative discipline.
• Hands-on experience using Python for data analysis, modeling, or quantitative testing.
• Strong analytical skills and the ability to independently evaluate complex methodologies.
• Strong written communication skills with the ability to turn technical analysis into clear conclusions.
• Exposure to machine learning or AI modeling is a plus.

About the Opportunity:

This position offers the opportunity to work across a broad range of models and business areas while continuing to develop expertise in model risk and quantitative validation within the financial services industry.