Model and Data Manager - Fraud Department - Vice President

We are seeking a Fraud Department - Model and Data Manager, Vice President.

In the Technology division, we leverage innovation to build the connections and capabilities that power our Firm, enabling our clients and colleagues to redefine markets and shape the future of our communities. This is a Lead Data & Analytics Engineering position at the Vice President level, which is part of the job family responsible for providing specialist data analysis and expertise that drive decision-making and business insights as well as crafting data pipelines, implementing data models, and optimizing data processes for improved data accuracy and accessibility, including applying machine learning and AI-based techniques.

Since 1935, Morgan Stanley is known as a global leader in financial services, always evolving and innovating to better serve our clients and our communities in more than 40 countries around the world.

Position Overview

The Model and Data Manager is a key leadership role responsible for the effective onboarding and monitoring of fraud detection and prevention models and data sources. This position will serve as the primary liaison between the Fraud Department and the Model Risk Management (MRM) function, ensuring all fraud models are compliant, robust, and aligned with organizational risk appetite. The VP will drive continuous improvement in model governance and operational excellence. This position reports directly to the Head of Fraud Analytics and Data Science.

What you'll do in the role:

• Data Onboarding: Working with Fraud Leadership and technology partners, identify and onboard new and ongoing data sources as required to satisfy Fraud data needs to support ongoing Fraud efforts, and enable Fraud coverage of new products and capability launches for the Firm.
• Model Onboarding: Oversee the end-to-end onboarding process for new internally developed and vendor supplied models, updated fraud models, including coordination with data science, technology, and business teams. Ensure all models meet regulatory and internal standards prior to deployment.
• Model Monitoring: Lead the ongoing performance monitoring of all fraud models, including periodic validation, back-testing, and performance reporting. Identify model drift, degradation, or emerging risks and coordinate timely remediation.
• Collaboration with Model Risk Management: Serve as the primary point of contact with the Model Risk Management function. Facilitate model validation, inventory management, control testing, and documentation updates to meet organizational and regulatory requirements. Ensure documentation covers assumptions, limitations, risk controls, data lineage, and governance touchpoints.
• Governance and Compliance: Govern the end-to-end model life cycle including development, validation, implementation, change management, monitoring, and decommissioning, ensuring robust controls at each stage. Ensure adherence to all internal model governance policies and external regulatory guidelines. Prepare and present model performance and risk assessments to senior management and governance committees.
• Stakeholder Engagement: Work cross-functionally with fraud operations, analytics, IT, compliance, and audit teams to ensure model integrity, transparency, and accountability.
• Team Leadership: Lead, mentor, and develop a team of fraud model analysts and specialists. Foster a culture of innovation, collaboration, and continuous learning.
• Innovation: Assess and guide the adoption of innovative modeling techniques (e.g., AI/ML) within the governance framework. Stay abreast of emerging risks, new fraud typologies, evolving regulatory expectations, and best practices in model risk for financial crimes and fraud detection.

• Bachelor's or Master's degree in quantitative field (e.g., Statistics, Mathematics, Data Science, Engineering, or related discipline).
• 10+ years of experience in fraud risk management, model risk management, or related analytical functions within the financial services industry.
• Strong expertise in model development, validation, and governance processes.
• Demonstrated experience leading cross-functional teams and managing complex projects.
• Excellent communication, stakeholder management, and presentation skills.
• In-depth knowledge of regulatory requirements related to model risk (e.g., SR 11-7, OCC 2011-12) is highly desirable.

• Proficiency in data analytics tools and programming languages (e.g., Python, R, PySpark, etc).
• Demonstrated experience with advanced fraud detection technologies, such as machine learning and artificial intelligence.
• Ability to manage competing priorities in a fast-paced environment.