FCC Modeling and Analytics (Finance)

As Director of Financial Crimes Modelling and Analytics, you will act as a model owner for Mizuho America's Financial Crimes ("FCC") Models, which include Transaction Monitoring, Real-Time Message Screening, and Customer Screening Systems models. Your primary responsibility will be to ensure the appropriate configuration of the Real-Time Message Screening Model and act as team lead for daily and periodic tuning and watchlist management of the model.

This role is hybrid and based in our New York City Office

Key Responsibilities

• Act as team lead for the daily BAU function for Mizuho's Real-Time Screening System. Oversee the daily BAU functions associated with watchlist updates and daily tuning efforts for false positive hit reduction measures.
• Perform periodic and event-driven tuning and optimization of FCC models using quantitative analysis and ATL/BTL to determine appropriate configuration settings and thresholds values while consideration AML and OFAC Risk Assessments.
• Conduct periodic Productivity Assessments to evaluate the continued relevancy of detection scenarios, underlying rules, threshold values, parameters, and assumptions, ensuring that the monitoring systems remain effective and aligned with current risks.
• Overseeing the annual 504 Certifications process by reviewing risk assessments and validations of the models according to NYDFS 504 requirements and internal guidelines.
• Collaborate with key stakeholders to develop controls and validate the integrity, accuracy, and quality of FCC data, ensuring accurate and complete data flows through the Transaction Monitoring and Filtering Program.
• Coordinate end-to-end, pre-and post-implementation testing of FCC Models, including reviews of model governance, performance testing, implementation accuracy, data integrity, and conceptual soundness.
• Maintain and update model documentation, standards, procedures, and other relevant documentation as needed, ensuring that all procedures and guidelines are current and effectively communicated to all relevant parties.
• Liaise with Model Risk Management and Head Office regarding updates to models, ensuring that all changes are accurately documented and communicated to relevant stakeholders to maintain model integrity and compliance.
• Execute the model risk framework and standards for the AML and Sanctions models in accordance with the Mizuho Model Risk Policy.

Qualifications

• 10+ years of experience in modelling and analytics, including significant experience in financial crimes analytics or related field.
• Bachelor's degree in business, analytics, computer science, statistics, mathematics, or demonstrable work experience in relevant field.
• Professional certifications in financial crimes (e.g. ACAMS, CFCS, CFE) preferred.
• Significant demonstrable experience in setting the algorithmic configuration and creating and managing false positive suppression rules and exceptions in Fircosoft or equivalent screening system.
• Experience with watchlist management and vendor management for Real-Time Message Screening Systems.
• Experience tuning and optimizing models for Transaction Monitoring, Transaction and Customer Screening, and/or Customer Risk Rating.
• Experience with data quality and data lineage analysis, ensuring that all data processes meet the highest standards and support organizational objectives is preferred.
• Proficiency with Microsoft Excel and Word.
• Experience with business intelligence tools like Power BI, Tableau, Spotfire, or Looker is preferred.

The expected base salary ranges from $150,000.00 - $225,000.00. Salary offers are based on a wide range of factors including relevant skills, training, experience, education, and, where applicable, certifications and licenses obtained. Market and organizational factors are also considered. In addition to salary and a generous employee benefits package, including but not limited to Medical, Dental and 401k that begin on day one of employment, successful candidates are also eligible to receive a discretionary bonus.

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