VP, Data Analyst – Anti Financial Crime, Group Retail, COO

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VP, Data Analyst – Anti Financial Crime, Group Retail, COO

Company Overview
This leading bank is a prominent financial institution in Asia with a global network across 19 countries and territories. With over 80 years of history, the bank is committed to offering comprehensive financial services while maintaining core values of being Honorable, Enterprising, United, and Committed. Its Retail function provides tailored financial solutions to customers, helping them manage their finances and achieve their financial goals. The COO Office works to safeguard business operations and manage risk across various departments.

Job Description
The VP, Data Analyst for Anti Financial Crime (FCR) will play a pivotal role in the development and maintenance of analytical models aimed at addressing compliance and anti-money laundering (AML) risks. This position requires collaboration with internal stakeholders to support model performance, participate in special projects, and ensure governance for models related to financial crime prevention.

Key Responsibilities

• Model Maintenance & Development: Collaborate with business analysts to understand model performance feedback, retrain models to prevent drift, and build new models to address emerging FCR challenges.
• Support for Special Projects: Assist in addressing high-priority initiatives, including regulatory requests and intelligence gaps. Stay updated with advancements in data science and analytics to recommend best practices.
• Model Governance: Contribute to model governance processes, ensuring models comply with ethical standards and regulatory requirements.
• Stakeholder Engagement: Work closely with end-users, business analysts, and data scientists to develop, test, and deploy FCR models that meet business needs and regulatory standards.

Job Requirements

• Experience: 6-8 years in technology and 2-4 years of data science/analytics experience, with a preference for experience in AML, FCR, or compliance risk analytics.
• Technical Skills: Expertise in R, SAS, SQL, Python, and machine learning tools such as Pandas, Keras, TensorFlow, and Scikit-learn. Familiarity with BI tools like Power BI, Qlik, or Tableau is advantageous.
• Education: A Bachelor’s degree in Computer Science, Engineering, Statistics, Mathematics, or Business Analytics, or equivalent experience.
• Additional Skills: Proven project management skills, proficiency in data wrangling, machine learning, natural language processing, network analysis, and the ability to effectively communicate technical results to business users.

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