At Schwab, you are empowered to make an impact on your career. Here, innovative thought meets creative problem solving, helping us “challenge the status quo” and transform the finance industry together.

Model Risk Oversight is a strategic function within the broader Corporate Risk Management umbrella that utilizes a broad spectrum of models to create innovative products for our clients, and to prudently manage our financial risk using sophisticated quantitative approaches. The Model Risk Oversight team plays a key role in identifying, reviewing, and monitoring all the models at the company.

We are hiring an experienced model validator at a Senior Manager level (individual contributor) to conduct validations of fraud models and make additional contributions to the Model Risk Oversight team.

The job responsibilities will include, but not be limited to:

• Performing model validations following guidelines based on SR 11-7, to include an assessment of model usage, documentation, conceptual soundness, data integrity, the control environment, and the software environment.
• Presenting work through formal model validation reports, as well as through verbal presentations to model owners and senior management.
• Working effectively as a team member with other analysts at the company, as well as with external consultants.
• Evaluating model performance monitoring reports and conducting model annual reviews.

This will be an individual contributor role.

Required Qualifications:

• Advanced degree in quantitative discipline (data science, statistics, mathematics, physics, engineering).
• 3-7+ years of work experience in quantitative modeling.
• Working knowledge of fraud models.
• Advanced skill with one or more analytical tools, such as R, MATLAB, or Python and machine learning packages such as Scikit-learn or similar.

Preferred Qualifications:

• Model validation experience.
• Strong oral and written communication skills.
• Excellent people skills.
• Comfortable working with large and unstructured data sets.
• Experience with the development and use of machine learning models.
• Ph.D. in quantitative discipline (data science, statistics, mathematics, physics, engineering).
• Professional certifications: CFA, FRM, CFCS or similar.
• Knowledge of model governance processes and regulatory requirements for large US banks.