Morgan Stanley2 followers13 jobs

Model and Data Manager - Fraud Department - Vice President

SeniorFull-timeHybridNew South Wales, AustraliaPosted 4 months agoVerified 2 weeks ago
Pending fitX of Y criteria met

About the job

The Fraud Department – Model and Data Manager position at Morgan Stanley is a vital leadership opportunity that focuses on onboarding and monitoring fraud detection models and data sources. This role plays a crucial part in ensuring compliance and operational excellence while driving continuous improvement in model governance. The team thrives on collaboration, innovation, and a commitment to excellence, creating an environment where everyone can contribute to shaping the future of financial services.

You'll be responsible for

📥

Data onboarding

Working with Fraud Leadership and technology partners to identify and onboard new data sources to support ongoing Fraud efforts.
📈

Model monitoring

Leading the ongoing performance monitoring of all fraud models, including validation and performance reporting.
🤝

Stakeholder engagement

Collaborating cross-functionally with fraud operations, analytics, IT, compliance, and audit teams to ensure model integrity.

Key criteria

📊

10+ years in fraud risk management

Extensive experience in fraud risk management within financial services.

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🔍

Model development and validation expertise

Strong background in model governance and validation processes.

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🤝

Cross-functional team leadership

Demonstrated ability to lead and manage diverse teams.

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View more

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5 criteria for this job
10+ years in fraud risk management
Extensive experience in fraud risk management within financial services.

View

Model development and validation expertise
Strong background in model governance and validation processes.

View

Cross-functional team leadership
Demonstrated ability to lead and manage diverse teams.

View

Proficiency in data analytics tools
Experience with tools like Python, R, and machine learning techniques.

View

Knowledge of regulatory requirements
In-depth understanding of model risk regulations and compliance.

View

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