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Form of workJob Description:
Global Financial Crimes Automated Detection & Monitoring team seeks a Quantitative Scenario Analyst - Anti-Money Laundering (AML) to conduct independent testing and review of complex models used to monitor and mitigate money laundering risk. These scenarios identify potentially suspicious behaviors for further evaluation by our in-house investigation teams. The candidate should exhibit familiarity with industry practices and have knowledge of up-to-date AML techniques. The candidate should be able to provide both thought leadership and hands-on expertise in methodology, techniques, and processes in overseeing statistical and machine learning models to manage the bank's AML models and model systems.
The Role will interact with a wide variety of stakeholders including Financial Crimes Investigations, business risk managers, model developers, model risk management, model implementation, and technology teams.
The qualified candidate will be responsible for overseeing a broad range of model risk oversight activities, including:
• Performing in-depth analysis on the Bank's AML model suite and clearly articulating a holistic picture of model performance, monitoring and targeted assessments.
• Performing independent model validation, annual model review, ongoing monitoring report review, required action item review, and peer review.
• Review, critical assessment, and challenge of AML models on conceptual soundness, assumptions and limitations, data, developmental evidence in support of modeling choices, performance, implementation, and documentation
• Communicating model performance to model stakeholders, including risk management, model development, model risk, and senior management with clear conclusions regarding accuracy and remediation areas as required
• Providing hands-on leadership for projects pertaining to AML Models to effectively challenge and influence the strategic direction and tactical approaches of these projects.
• Communicating and working directly with relevant modeling teams and their corresponding stakeholders
• Acts as a senior level resource or resident expert on analytic/quantitative modeling techniques used for Anti-money laundering.
Required Skills:
• Bachelor's Degree (or Higher) in a quantitative field such as Mathematics, Computer Science, Statistics or 5+ years of experience in oversight and understanding of Anti-Money Laundering models and systems
• Strong familiarity with the industry practices in the field and knowledge of up-to-date Anti Money Laundering techniques
• Logical thinker who is able to make rational, evidence-based decisions whilst knowing when to escalate, as necessary
• Fluency in Python, SAS and SQL
• Proficiency in Microsoft Excel / data analytics; ability to analyze and manipulate large data sets
• Excellent written and oral communication skills with stakeholders of varying analytic skill and knowledge levels.
• Flexible to perform other functions as requested by management
• CAMS certification (preferred)
Shift:
1st shift (United States of America)
Hours Per Week:
40
Global Financial Crimes Automated Detection & Monitoring team seeks a Quantitative Scenario Analyst - Anti-Money Laundering (AML) to conduct independent testing and review of complex models used to monitor and mitigate money laundering risk. These scenarios identify potentially suspicious behaviors for further evaluation by our in-house investigation teams. The candidate should exhibit familiarity with industry practices and have knowledge of up-to-date AML techniques. The candidate should be able to provide both thought leadership and hands-on expertise in methodology, techniques, and processes in overseeing statistical and machine learning models to manage the bank's AML models and model systems.
The Role will interact with a wide variety of stakeholders including Financial Crimes Investigations, business risk managers, model developers, model risk management, model implementation, and technology teams.
The qualified candidate will be responsible for overseeing a broad range of model risk oversight activities, including:
• Performing in-depth analysis on the Bank's AML model suite and clearly articulating a holistic picture of model performance, monitoring and targeted assessments.
• Performing independent model validation, annual model review, ongoing monitoring report review, required action item review, and peer review.
• Review, critical assessment, and challenge of AML models on conceptual soundness, assumptions and limitations, data, developmental evidence in support of modeling choices, performance, implementation, and documentation
• Communicating model performance to model stakeholders, including risk management, model development, model risk, and senior management with clear conclusions regarding accuracy and remediation areas as required
• Providing hands-on leadership for projects pertaining to AML Models to effectively challenge and influence the strategic direction and tactical approaches of these projects.
• Communicating and working directly with relevant modeling teams and their corresponding stakeholders
• Acts as a senior level resource or resident expert on analytic/quantitative modeling techniques used for Anti-money laundering.
Required Skills:
• Bachelor's Degree (or Higher) in a quantitative field such as Mathematics, Computer Science, Statistics or 5+ years of experience in oversight and understanding of Anti-Money Laundering models and systems
• Strong familiarity with the industry practices in the field and knowledge of up-to-date Anti Money Laundering techniques
• Logical thinker who is able to make rational, evidence-based decisions whilst knowing when to escalate, as necessary
• Fluency in Python, SAS and SQL
• Proficiency in Microsoft Excel / data analytics; ability to analyze and manipulate large data sets
• Excellent written and oral communication skills with stakeholders of varying analytic skill and knowledge levels.
• Flexible to perform other functions as requested by management
• CAMS certification (preferred)
Shift:
1st shift (United States of America)
Hours Per Week:
40
