AVP, Model Validation
Not Disclosed•Full-TimeHybrid
location_onNYU Paulson Center, 181, Mercer Street, University Village, Manhattan, New York County, New York, 10012, United States
The AVP, Model Validation serves as a key leader within the Risk organization, ensuring that Synchrony's credit acquisition, account management, fraud, and marketing models adhere to rigorous Model Risk Management policies and federal regulations (OCC2011-12/SR 11-7). This position is designed for a high-expertise professional who operates with minimal technical supervision to lead validation projects, take full accountability for results across diverse model categories, and drive meaningful contributions to the firm's risk framework.
In this role, you will partner closely with business teams to uncover and highlight model risks while collaborating with the broader Risk organization to validate the accuracy and performance of statistical, AI, and ML models. You will act as a subject matter expert, keeping pace with the latest developments in academia and the regulatory environment to provide guidance on model risk management solutions. The role involves supporting regulatory examinations and internal audits, ensuring that the modeling process remains robust and compliant.
At Synchrony, we are proud to offer flexibility. Our way of working allows you the option to work from home near one of our Hubs or come into one of our offices. Occasionally, you will be required to commute or travel for in-person engagement activities, such as business or team meetings, training, and culture events.
When you join Synchrony, you become part of an inclusive culture where individual skills, experience, and voices are not only heard but valued. Together, we are building a future where everyone can belong, connect, and turn ideals into action. More than 50% of our workforce is engaged in our Employee Resource Groups (ERGs), where community and passion intersect to offer a safe space to learn and grow.
We ensure all qualified applicants receive consideration for employment without regard to age, race, color, religion, gender, sexual orientation, gender identity, national origin, disability, or veteran status. We are proud to maintain an award-winning culture for all.
Legal authorization to work in the U.S. is required for this position; we will not sponsor individuals for employment visas. As part of the onboarding process, candidates must be willing to take a drug test, submit to a background investigation, and provide fingerprints. Additionally, all applicants must satisfy the requirements of Section 19 of the Federal Deposit Insurance Act.
If you require a reasonable accommodation to apply for a job or to perform your job due to a disability, please contact our Career Support Line at 1-866-301-5627 (8am – 5pm Monday to Friday, Central Standard Time). Representatives are available to discuss your specific situation and assist with changes to the application process or work procedures.
Work model: Hybrid
NYU Paulson Center, 181, Mercer Street, University Village, Manhattan, New York County, New York, 10012, United States
New York, New York
Strong knowledge of Regulatory requirements for Model Risk Management with proven track records of delivering Regulatory requirements. 5+ years of proven experience 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 people and project management, including demonstrated ability to develop actionable plan to meet high level objectives, strong execution, and timeline sensitive deliverables. Knowledge of Credit Card/Consumer Finance products and business model. Experience with Machine Learning / AI methodologies and related applications, including generative AI model validation and validation framework development. Excellent written and oral communication and presentation skills.
Skills: Python, R, Sas, SQL, Spark, Data Lake, H2o, Sagemaker, Aws, Machine Learning.
Education: Master's degree in Statistics, Mathematics, Data Science or related quantitative field; High school diploma or equivalent.
