AI/ML Model Risk Validator

About the role

As a Model Risk Validator on the Data Science team at Rippling, you will be responsible for independently validating and challenging credit and financial risk models used across our products, including Corporate Card, Bill Pay, Payroll, and Employer of Record. You will evaluate models built by Data Science teams to ensure they are conceptually sound, correctly implemented, well-calibrated, and robust under different economic and portfolio conditions.

This role sits at the intersection of model risk governance and advanced data science, requiring strong technical depth, sound judgment, and the ability to engage constructively with model builders while maintaining independent challenge standards.

What you will do

Validate AI/ML risk models: Independently assess underwriting, limit assignment, loss forecasting, early warning, and portfolio monitoring models.

Challenge model design and assumptions: Evaluate feature engineering, data quality, methodology choices, and conceptual soundness of statistical and ML models.

Assess model performance: Test discrimination, calibration, stability, drift, and robustness across segments and economic environments.

Validate implementation accuracy: Reconcile model logic, code, and production pipelines against documented specifications.

Build validation frameworks: Develop scalable tools, tests, and templates for repeatable model validation and monitoring.

Support model governance: Produce clear validation reports, findings, and documentation for internal risk committees and audit/regulatory review.

Partner with Data Science teams: Work closely with model developers to communicate findings, track remediation, and improve model design and controls.

Maintain model inventory oversight: Support tracking of model lifecycle, validation schedules, and risk ratings across the portfolio.

What you will need

3–7 years of experience in model validation, model risk management, quantitative risk, or data science in financial services

Strong background in credit risk, financial modeling, or applied machine learning

Hands-on experience with statistical and ML models (e.g., logistic regression, gradient boosting, scorecards, time-series models)

Strong Python and SQL skills for model analysis, testing, and validation

Deep understanding of model risk concepts (e.g., SR 11-7 principles or equivalent governance frameworks)

Strong ability to critically evaluate models and challenge assumptions constructively

Experience working with large, complex datasets in fintech, banking, or SaaS environments

Bachelor’s degree in a quantitative field (Master’s preferred)

Additional Information

Rippling highly values having employees working in-office to foster a collaborative work environment and company culture. For office-based employees (employees who live within a defined radius of a Rippling office), Rippling considers working in the office, at least three days a week under current policy, to be an essential function of the employee's role.

About Rippling

Rippling gives businesses one place to run HR, IT, and Finance. It brings together all of the workforce systems that are normally scattered across a company, like payroll, expenses, benefits, and computers. For the first time ever, you can manage and automate every part of the employee lifecycle in a single system.

Take onboarding, for example. With Rippling, you can hire a new employee anywhere in the world and set up their payroll, corporate card, computer, benefits, and even third-party apps like Slack and Microsoft 365—all within 90 seconds.

Based in San Francisco, CA, Rippling has raised $1.4B+ from the world’s top investors—including Kleiner Perkins, Founders Fund, Sequoia, Greenoaks, and Bedrock—and was named one of America's best startup employers by Forbes.

We prioritize candidate safety. Please be aware that all official communication will only be sent from @Rippling.com addresses.