AVP, Model Monitoring Analytics

Job Description Role Summary/Purpose

The AVP, Model Monitoring Analytics Lead is a key stakeholder in the ongoing risk oversight of deployed models across the organization. This role serves as a subject matter expert in model monitoring best practices and governance standards, ensuring early identification of model risk through data-driven diagnostics and analyses. Leveraging advanced analytics, visualization tools, and regulatory understanding, this role ensures models remain robust, interpretable, and aligned with evolving business and regulatory expectations-ultimately safeguarding the integrity of the firm's model risk management lifecycle.

We're proud to offer you 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 or travel for in person engagement activities such as business or team meetings, training and culture events.

• Support the development and execution of the model monitoring roadmap, ensuring the practices are forward-looking, risk-informed, and aligned with business priorities and regulatory expectations.

• Lead performance oversight for high-impact models, prioritizing monitoring efforts based on risk, materiality, and business impact-ensuring the right models receive the right level of scrutiny. Actively identifying and probing both explicit and implicit assumptions that could lead to model failure.

• Drive complex performance investigations, including monitoring, reporting, and flagging performance issues to support identification of root causes and business implications and guide coordinated corrective actions.

• Oversee the design and evolution of monitoring tools, dashboards, and alerting systems that deliver timely, actionable insights to stakeholders and support informed decision-making

• Maintain enterprise-wide monitoring standards, defining performance thresholds, stability indicators, and explainability requirements that reflect both regulatory guidance and internal risk appetite.

• Manage the end-to-end project lifecycle for model monitoring activities across all in-scope models, ensuring adherence to defined schedules and governance standards.

• Serve as the coordinator for cross-functional teams involved in monitoring execution, including data science, model risk, compliance, model owners,

• Champion automation and scalability, leveraging technology and analytics to streamline recurring monitoring routines and ensure consistency, accuracy, and long-term efficiency.

• Interface with model owners, developers, and validators to ensure timely review and resolution of model monitoring issues.

• Act as a trusted advisor during audits and regulatory reviews, clearly articulating monitoring outcomes, risk mitigations, and the strength of the monitoring framework.

• Collaborate cross-functionally with model developers, users, validators, and risk officers to ensure that performance insights are integrated throughout the model lifecycle-from design to retirement.

• Continuously evolve the monitoring function, staying ahead of industry best practices, emerging risks, and new technologies to ensure our capabilities remain strong, relevant, and resilient.

• Perform other duties and/or special projects as assigned.

• Bachelor's degree and a minimum of 3 years of experience in model analytics, monitoring, validation, or credit risk strategy within a financial or regulated setting; or in lieu of a Bachelor's Degree, a High School Diploma/GED and a minimum of 6 years of experience in model analytics, monitoring, validation, or credit risk strategy within a financial or regulated setting.

• 3+ years of experience programming with SAS, SQL, Python, or other relevant languages

• Experience with utilizing model performance metrics such as KS, AUC, PSI, and statistical indicators of stability and drift.

• Experience with data visualization tools like Tableau, Power BI, or similar platforms to create actionable performance dashboards.

• Experience with synthesizing technical insights into business-aligned narratives and actionable recommendations.

• Ability and flexibility to travel for business as required

• Bachelor's degree in a quantitative field (e.g., Statistics, Mathematics, Engineering, Data Science, Economics, or related discipline).

• Advanced Master's degree or relevant advanced certification (e.g., CFA, FRM, data science bootcamps).

• Proven project management capabilities with experience leading multiple monitoring initiatives in parallel.

• Ability to synthesize complex technical findings into business-friendly communications and recommendations.

• Working knowledge of performance drift metrics, stability indicators, and key model health metrics.

• Experience managing multiple projects or monitoring streams, with attention to timelines, quality, and stakeholder expectations.

• Familiarity with modern machine learning models,…