Financial Crimes Senior Model Validator

Your Opportunity

At Schwab, you're 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 Corporate Risk Management that uses advanced quantitative methods and a broad range of models to support innovative client solutions and manage financial and reputational risk. The team identifies, reviews, and monitors models used across the organization.

We are seeking candidates for a Senior Manager individual contributor role focused on model validation and broader Model Risk Oversight initiatives. Candidates should have strong quantitative skills and business experience with fraud and anti-money laundering models. Less experienced candidates may be considered for junior roles.

Job responsibilities include:

• Validate models in accordance with SR 26-2, including model use, documentation, conceptual soundness, data integrity, controls, and software.
• Prepare model validation reports and present findings to model owners and senior management.
• Partner with internal analysts and external consultants on validation activities.
• Evaluate model performance monitoring and complete annual model reviews.

What You Have

Required Skills and Qualifications:

• 2+ years of experience in fraud monitoring and anti-money laundering.
• 5+ years of quantitative experience.
• Advanced degree in statistics, mathematics, physics, engineering, economics, or a related quantitative field.
• Knowledge of model governance and U.S. banking regulations.
• Proficiency in Python, SAS, R, or other statistical programming languages.

Preferred Skills and Qualifications:

• Ph.D. in quantitative fields, such as data science, statistics, mathematics, physics, or engineering.
• Experience developing and applying machine learning models.
• 5+ years of model validation experience.
• 2+ years of experience with digital assets.
• Experience working with large, unstructured data sets.
• Knowledge of digital asset regulations, fraud, and AML.
• CFA, FRM, ACAMS, or digital asset certifications.
• Quantitative finance experience.
• Strong verbal and written communication skills.
• Strong interpersonal skills.
• Excellent relationship-building skills.