Offer summary

Qualifications:

Bachelor's degree in data analytics or a related field., At least 7 years of experience in financial crimes and AML., Proficiency in SQL, Python, R, SAS, and Tableau., Strong understanding of AML/KYC regulations and practices..

Key responsabilities:

Analyze transactions, accounts, and third-party data to identify suspicious patterns and risks.

Utilize data analytics and visualization tools to support investigations.

Design and implement financial crime detection models.

Translate forensic analyses into actionable insights for investigators.

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 the 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. This role will be work from home (Poland).

Responsibilities:

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) to support investigations teams and decision-making processes.
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.

Qualifications:

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 the financial crimes / AML space.
Proficiency in data analytics tools such as SQL, Python, R, and SAS.
Experience with data visualization tools like Tableau.
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.
Advanced degree in a related field (e.g., Data Science, Statistics, Finance).