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Validator Quantitative Specialist

Job in Minneapolis, Hennepin County, Minnesota, 55400, USA
Listing for: U.S. Bank
Full Time position
Listed on 2026-06-06
Job specializations:
  • Finance & Banking
    Data Scientist, Financial Analyst, Financial Consultant, Risk Manager/Analyst
Salary/Wage Range or Industry Benchmark: 60000 - 80000 USD Yearly USD 60000.00 80000.00 YEAR
Job Description & How to Apply Below
## Validator Quantitative Specialist Apply locations:
Minneapolis, MN:
Charlotte, NC:
San Francisco, CAtime type:
Full time posted on:
Posted Yesterday time left to apply:
End Date:
June 17, 2026 (12 days left to apply) job requisition :
At U.S. Bank, we’re on a journey to do our best. Helping the customers and businesses we serve to make better and smarter financial decisions and enabling the communities we support to grow and succeed.  We believe it takes all of us to bring our shared ambition to life, and each person is unique in their potential. A career with U.S. Bank gives you a wide, ever-growing range of opportunities to discover what makes you thrive at every stage of your career.

Try new things, learn new skills and discover what you excel at—all from Day One.##
** Job Description
** This position is an individual contributor role residing within the Bank’s Second Line of Defense Risk Management and Compliance organization. Specifically, this position supports the Model Risk Management (“MRM”) program at the Bank. The overall MRM program is designed to provide governance and control around impactful institutional models per the Bank’s Model Risk Management Policy and Standards, and in adherence to applicable Regulatory Guidance.

The individual in this position works with Model Owners and Developers to validate models related to the Bank's Compliance and Financial Crimes groups, which can include Anti-Money Laundering (AML), Cybersecurity, Economic Sanctions/OFAC, and Fair Lending. The Analyst may also interface with key stakeholders, regulators, and internal auditors to discuss the justification and reasoning behind various validation and review outcomes.

During validation, the Analyst will independently challenge the model’s conceptual framework and methodology, data integrity and usage, implementation framework, process/governance, ongoing performance, etc. The Analyst will generate reporting to document and present observations made to model owners and developers for consideration, with any material concerns or weaknesses identified. Any items that require corrective actions to be taken by the model risk owners with then be tracked by the Analyst to remediation.
** Basic Qualifications**  - Bachelor’s degree in a quantitative field, and five or more years of relevant experience  OR  - MA/MS in a quantitative field, and three or more years of related experience  OR  - PhD in a quantitative field, and less than two years of related experience
** Preferred Skills/Experience
*** Self-motivated worker and comfortable working with varying levels of guidance.
* Strong mathematical and statistical modeling or model validation experience.
* Advanced understanding of applicable compliance laws, regulations, financial services, and regulatory trends that affect assigned line of business.
* Prior exposure to validation or development of models across BSA/AML, Cybersecurity, Fair Lending, Economic Sanctions, etc.
* An ability to build strong relationships with stakeholders across multiple levels (e.g., senior leadership, teammates, etc.), with different analytical backgrounds, and various model development/validation experience.
* Working knowledge of various Machine Learning methods, with working knowledge/experience.
* Use of AI tools as part of regular workflow
* Excellent verbal and written communication skills are a necessity (ability to explain complex ideas in simple, non-technical language).
* Able to perform complex mathematical analysis utilizing various statistical and mathematical methods or techniques including.
* Understanding of foundational theories and methodologies of varying structural complexities, including (but not limited to) multi-layered rule-based algorithms; standard statistical methods for the identification of outlier events; linear or logistic regression; natural language processing (NLP), and machine learning methods such as random forest, gradient boosting techniques, or other ensemble techniques.
* Ability to challenge either custom built or third-party proprietary vendor models.
* Can independently design and perform analytical tests, generate reporting that clearly articulates…
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