Remote Lead: Financial Model Risk Validator

Affirm; Nashville (TN); On-site; USD 164,000 - 245,000; Full time

Benefits offered by this job

Health coverage

Flexible Spending Wallets

Time off

ESPP

Job summary

Affirm is seeking a Lead Validator for Finance and Analytics models within the Model Risk Management team. You will perform independent validation of ALM, forecast, loan transition, and other analytics models, driving monitoring and governance across Finance and Growth Analytics.

You will lead end-to-end validation engagements for complex models, identify weaknesses, and clearly communicate findings to both technical and non-technical stakeholders, partnering with model owners and internal

Qualifications

• 4–6 years of professional experience in model validation or related fields.
• Strong knowledge of corporate finance, ALM, and forecasting.
• Experience with scripting languages and large datasets.
• Excellent communication with technical and non-technical stakeholders.

Responsibilities

• Validate financial and analytics models and monitor performance.
• Lead end-to-end validation engagements for high-complexity models.
• Identify weaknesses and articulate findings clearly to stakeholders.
• Collaborate across Finance, Quantitative Research, and Growth Analytics for remediation.
• Support remediation and strengthen model governance across teams.
• Coordinate with Internal Audit and Compliance to address requests.

Skills

Python scripting

SQL querying

Excel modeling

Databricks

Data analytics

Time-series forecasting

Machine learning concepts

Communication skills

Education

Quantitative degree (BS/MS/PhD)

Tools

Databricks

Excel

SQL tooling

Job description

Affirm is seeking a Lead Validator for Finance and Analytics models within the Model Risk Management team. You will perform independent validation of ALM, forecast, loan transition, and other analytics models, driving monitoring and governance across Finance and Growth Analytics.

You will lead end-to-end validation engagements for complex models, identify weaknesses, and clearly communicate findings to both technical and non-technical stakeholders, partnering with model owners and internal