VP, Model Validation and Validation COE

Role Summary/Purpose

The VP, Fraud/GEN AI Validation COE is responsible for performing model validation for all the fraud models, and ensure they are meeting the related MRM policies, standards, procedures as well as regulations (SR 11-7). In addition, this role will establish and maintain a validation center of excellence to support the model governance team in designing the quality assurance process and leading the execution across all validations, act as an incubation center to test and run innovation, provide standardized training and staff development, and support the improvement of the model risk professional practice to improve the model stakeholder experience.

This role requires high level of expertise with minimal technical supervision to serve as project lead as well as being accountable for validation results. The COE will closely partner with the model governance lead as well as other validation leads to drive tangible improvements to the model risk practice.

We’re proud to offer you choice and flexibility. At Synchrony, our way of working allows you to have the option to work from home near one of our Hubs or come into one of our offices. Occasionally you may be required to commute to our nearest office for in person engagement activities such as business or team meetings, training and culture events.

Candidate will be responsible for work related to model validation and validation COE, both of which are outlined below:

The candidate is expected to draw experience from validation to establish a validation center of excellence and support the model governance lead to drive best practice, specifically the objectives of the COE are:

• GEN AI Model Risk Management
  :
  Lead the creation and implementation of a comprehensive, end-to-end governance framework for Generative AI models, establishing clear standards, procedures, documentation templates, and process to effectively manage model risks such as hallucination, accuracy, and bias. This framework will enable the organization to consistently monitor and control these critical aspects throughout the model lifecycle, ensuring reliable and trustworthy AI outputs with disciplined and transparent oversight.

• Quality assurance and Capacity Planning
  :
  Establish and maintain a quality assurance process to thoroughly review and assess validation practices. Proactively challenge the status quo to identify gaps or improvement opportunities in validation efforts. Provide guidance on best practices, support capacity planning, and collaborate with the Model Governance team to recommend and implement enhancements that strengthen the overall validation framework.

• Strategy & Innovation
  :
  Serve as an incubation center to explore, test, and implement innovative approaches - leveraging Generative AI capabilities - to accelerate and improve the speed, efficiency, and quality of model validation processes.

• Professional Practice
  :
  Support the Model Governance team to improve 1
  
  LOD model owner experience and bring value focused validation practice.

Accountable for all fraud model risk management and drive the timeline and completion of the projects with minimal guidance.

Supervisory role working with junior reviewers in validation projects.

Handle escalation of issues and dispute with model owner level independently. See through the issues remediation, root cause analysis, and potential risk acceptance.

Support regulatory examinations and internal audits of the modeling process and selected models samples.

Perform other duties and/or special projects as assigned.

• Master's degree (or foreign equivalent) in Statistics, Mathematics, Data Science or related quantitative field and 5+ years' experience in model development / model validation experience in financial services, banking, or retail; in lieu of a Master's degree, 9+ years' experience in model development / model validation experience in financial services, banking, or retail

• 4+ years' hands‑on and proven experience with data science and statistical tools including Python, R, SAS, and SQL, SPARK, and Data Lake.

• 4+ years of statistical analysis and the handling large amounts of data and analyzing for trends.

• 4+ years' experience with the application of US regulatory requirements for Model Risk Management.

• Ability and flexibility to travel for business as required

• Strong knowledge of Regulatory requirements for Model Risk Management with proven track records of delivering Regulatory requirements.

• 5+ years in Model Risk Management in modeling and validation in the financial services industry including both analytic/modeling/quantitative experience and governance or other credit/financial discipline.

• Experience in GEN AI Model validation, framework establishment

• Experience in validation process automation and cycle time reduction leveraging GEN AI and other tools

• Experience in people and project management, including…