Job Description

We are seeking a highly skilled and experienced Senior Data Analyst to join our Financial Crimes / Anti-Money Laundering (AML) team. The ideal candidate will have a strong technical background and extensive experience in financial crimes and AML space. This role involves analyzing and interpreting transactions, accounts, customers, alerts, and third-party data using advanced statistical and analytical tools to identify potential suspicious patterns, anomalies, and risks.

• Analyze and interpret transactions, accounts, customers, alerts, and third-party data using statistical and analytical tools to identify potential suspicious patterns, anomalies, and potential risks.
• Utilize data analytics tools (e.g., SQL, Python, R, SAS) and data visualization tools (e.g., Tableau, PowerPoint) to support investigations teams and decision-making processes.
• Perform data ETL, cleaning, and validation.
• Design and implement financial crime detection models and scenarios.
• Conduct root cause analyses on financial crime incidents to enhance detection and prevention strategies.
• Translate data-driven forensic analyses into actionable insights for investigators, enabling effective and efficient investigations.

• At least a bachelor's degree in data analytics or a related field.
• At least 7 years of experience in the field.
• Proven experience in financial crimes/AML space.
• Proficiency in data analytics tools such as SQL, Python, R, and SAS.
• Experience with data visualization tools like Tableau and PowerPoint.
• Strong understanding of AML/KYC regulations and practices.
• Experience in designing and implementing financial crime detection models and scenarios.
• Ability to conduct root cause analyses on financial crime incidents.
• Excellent analytical and problem-solving skills.
• Strong communication skills, with the ability to translate complex data into actionable insights.

• Experience working in a major financial institution or consulting firm preferred.
• Advanced degree in a related field (e.g., Data Science, Statistics, Finance) preferred.

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