More jobs:
Manager, Model Risk Management
Job in
Denver, Denver County, Colorado, 80285, USA
Listed on 2026-02-16
Listing for:
The Clearing House
Full Time
position Listed on 2026-02-16
Job specializations:
-
IT/Tech
Data Science Manager, Data Security
Job Description & How to Apply Below
Grace NY Office time type:
Full time posted on:
Posted 3 Days Agojob requisition :
JR100044
*
* Position Summary:
** The Manager, Model Risk Management plays a central leadership role in overseeing enterprise-wide model risk activities across all phases of the model lifecycle. This position expands beyond day-to-day analytical responsibilities to include governance ownership, cross-functional collaborations, vendor model oversight, and enhanced subject-matter expertise in AI/ML and Generative AI (GenAI) model risks.
The Manager will ensure strong regulatory alignment, transparent reporting, and effective risk mitigation practices for operational, analytical, financial and AI-driven models used across the organization.
** Essential Functions and Responsibilities
*** Oversees model risk governance across all business units to ensure alignment with the current Model Risk framework.
* Maintains and updates Model Risk Management Framework, and policies as required by industry changes.
* Advise model developers and business units on model expectations.
* Conduct independent validation activities covering conceptual soundness, methodology, performance testing, data quality, sensitivity/benchmark analysis, and outcomes analysis.
* Prepare comprehensive validation documents including evidence of validation activities supporting final recommendations.
* Supervise more complex reviews of AI/ML and GenAI systems, including robustness testing, explainability assessment, hallucination/response integrity evaluation, drift analysis, and model fairness or bias testing.
* Review and challenge validation work performed by peers or third-parties; provide prescriptive feedback and ensure consistency and quality across deliverables.
* Lead governance activities for the full model lifecycle—including identification, risk rating, validation, approval, monitoring, periodic review, change management, and retirement—ensuring compliance with internal policies and regulatory expectations.
* Monitor findings and remediations related to Model Validations.
* Maintain enterprise model inventory and ensure documentation quality, completeness, and audit readiness for all models.
* Assess and approve model monitoring submissions.
* Contribute to the enhancement and implementation of the Model Risk Management (MRM) frameworks, processes, and standards.
* Collaborate with cross-functional stakeholders to ensure model risks are appropriately governed and mitigated.
** Qualifications
Required:
*** Bachelor’s degree in mathematics, statistics, finance, computer science, data science, engineering, or related quantitative field;
Master’s, MBA, or Doctorate preferred.
* 7–10 years of experience in model risk management, model validation, quantitative analytics, model or AI/ML development, risk governance, or equivalent.
* Strong understanding of model lifecycle governance, regulatory expectations (The Fed), and industry model risk frameworks.
* Hands-on experience assessing or validating traditional models as well as AI/ML or GenAI models.
* Knowledge of model explainability tools, AI ethics, and responsible AI principles.
* Ability to interpret, challenge, and communicate complex modeling concepts to both technical and non-technical audiences.
* Ability to work collaboratively with others to build strong relationships across the organization.
* Proficiency in analytical tools such as Python or R; strong Excel and dashboarding skills (e.g., Tableau).
* Excellent written and verbal communication skills, including experience preparing committee-level reporting
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* Preferred Qualifications:
*** Experience reviewing or overseeing vendor-provided models and third-party risk documentation, a plus
* Familiarity with model risk supervision within financial services, payments, or clearinghouse environments.
* Knowledge of model explainability tools, AI ethics, and responsible AI principles.
* Experience mentoring or supervising analysts or junior staff.
** Physical Demands and Work Environment**:
The working environment is generally favorable. Lighting and temperature are adequate, and there…
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