Senior Data Analyst-Financial Crimes/AML
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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