Quantitative Analyst – Model Risk

Forbes Technical Consulting LLC; Chicago (IL); Hybrid; USD 90,000 - 110,000; Full time

Benefits offered by this job

Medical, dental, and vision coverage

Life and disability insurance

Parental leave

401(k) with employer match

Flexible spending accounts

Transit/commuting benefits

Job summary

Forbes Technical Consulting LLC in the Chicago area seeks a Quantitative Analyst to join the Model Risk Management team. The role focuses on independent validation and governance of statistical, econometric, mathematical, and ML models across the organization with a strong Python component.

The ideal candidate holds a Master’s or PhD in a quantitative field, with 1–3 years of relevant experience and 1–2+ years of hands-on Python.

Qualifications

• Master’s degree or PhD in a quantitative discipline.
• 1–3 years of relevant quantitative analysis or validation experience.
• 1–2+ years of hands-on Python experience.
• Exposure to financial risk models and regulated environments.

Responsibilities

• Independently validate statistical, econometric, mathematical, machine learning, and qualitative models.
• Evaluate models for conceptual soundness, implementation accuracy, data integrity, governance, and performance.
• Develop and execute model validation approaches and benchmark models based on risk.
• Perform quantitative testing including back-testing, sensitivity and scenario analysis.
• Review models supporting credit risk, CECL, stress testing, AML/BSA, fair lending, liquidity, and valuation.
• Prepare detailed validation documentation outlining methodology, testing, findings, conclusions, and recommendations.
• Partner with developers, stakeholders, and risk teams to drive issues to resolution.
• Support governance activities and reporting for leadership and regulatory examinations.
• Identify opportunities to automate model validation processes.
• Assist with evaluation/validation of emerging AI and machine learning models.

Skills

Analytical thinking

Quantitative analysis

Communication of findings

Education

Master’s degree or PhD in Mathematics, Statistics, Economics, Data Science, or related quantitative field

Tools

Python

Job description

We are seeking a Quantitative Analyst to join the Model Risk Management team of a large financial services organization in the Chicago area.

This position will focus on the independent validation and governance of statistical, econometric, mathematical, and machine learning models used across the organization. The ideal candidate will have a strong quantitative background, hands‑on Python experience, and exposure to model development or validation within banking, financial services, or another highly regulated environment.

This is an excellent opportunity for someone early in their quantitative risk career who wants exposure to a broad portfolio of enterprise models and the opportunity to work closely with experienced risk professionals and senior stakeholders.

What You’ll Do

• Independently validate statistical, econometric, mathematical, machine learning, and qualitative models
• Evaluate models for conceptual soundness, implementation accuracy, data integrity, governance, and performance
• Develop and execute model validation approaches and benchmark models based on model complexity and risk
• Perform quantitative testing including back‑testing, sensitivity analysis, scenario analysis, and benchmarking
• Review models supporting areas such as credit risk, CECL, stress testing, AML/BSA, fair lending, customer risk, liquidity, and valuation
• Prepare detailed model validation documentation outlining methodology, testing, findings, conclusions, and recommendations
• Partner with model developers, business stakeholders, and risk teams to investigate findings and drive issues toward resolution
• Support model governance activities and reporting for senior leadership, risk committees, and regulatory examinations
• Identify opportunities to automate and improve model validation processes
• Assist with the evaluation and validation of emerging AI and machine learning models

What We’re Looking For

• Master’s degree or PhD in Mathematics, Statistics, Economics, Data Science, or another quantitative discipline
• Approximately 1–3 years of relevant experience in quantitative analysis, data science, model development, or model validation
• 1–2+ years of hands‑on Python experience
• Understanding of statistical modeling, econometrics, machine learning, and quantitative testing methodologies
• Experience or exposure to financial risk models such as:
• Consumer or commercial credit risk
• CECL
• Stress testing
• BSA/AML
• Ability to evaluate model assumptions, methodology, data, implementation, and performance
• Strong analytical, problem‑solving, and technical documentation skills
• Ability to clearly communicate quantitative findings to both technical and nontechnical stakeholders

Preferred Qualifications

• Previous experience within banking, financial services, risk management, or another regulated industry
• Direct experience with model development or independent model validation
• Exposure to AI or machine learning model validation
• Familiarity with financial‑services model risk management and regulatory expectations
• Experience automating quantitative analysis or validation processes using Python

Compensation

Base Salary: $90,000–$110,000 annually, based on qualifications, skills, and experience.

The position is also eligible for an annual bonus.

The organization offers a comprehensive benefits package that includes medical, dental, and vision coverage; life and disability insurance; parental leave; employee assistance programs; 401(k) options with employer match; flexible spending accounts; transportation/commuting benefits; and additional employee benefits.

Location & Schedule

This is a full‑time, hybrid position in the Chicago area, with an expected in‑office component of approximately three days per week.

We are committed to providing equal employment opportunities to all qualified applicants without regard to race, color, religion, sex, age, national origin, disability, veteran status, genetic information, or any other characteristic protected by applicable law.