AML Data Analyst (JD#11238)

SCIENTE INTERNATIONAL PTE. LTD.; Singapore; On-site; SGD 90,000 - 130,000; Full time

Job summary

SCIENTE INTERNATIONAL PTE. LTD. in Singapore seeks a detail-oriented analytics professional to support Anti-Financial Crime data analytics, surveillance model validation, testing, and system enhancements.

This role helps ensure accuracy, regulatory compliance, and modernization including AI/ML-enabled capabilities. Responsibilities include data analysis, alert refinement, model validation across Core Engine systems, and collaborating with IT for improvements while maintaining data quality and

Qualifications

• Bachelor's degree in a related discipline.
• Minimum 5 years' experience in the financial services industry.
• Hands-on experience with AML systems, transaction monitoring and Anti-Financial Crime compliance.
• Experience in data analytics, reporting, and project support or coordination.
• Proficiency in Microsoft Excel, SQL and Python.
• Experience with data visualization tools such as Power BI or Qlik.
• Strong analytical and critical thinking skills.

Responsibilities

• Conduct data analysis to identify patterns and anomalies related to money laundering, fraud and sanctions.
• Assist in reviewing and refining alerts, reducing false positives, and improving detection efficiency.
• Support monitoring and validation of AFC surveillance models and Core Engine systems, ensuring data accuracy and performance tracking.
• Support data analytics activities including alert performance review, anomaly identification, and development or quality assurance of dashboards / reports.
• Perform data extraction, validation and analysis using SQL or relevant analytics tools where applicable.
• Support multi‑system AML testing across Core Engine platforms, primarily in user acceptance testing (UAT), including defect tracking and validation.
• Conduct regular testing and validation of AFC monitoring models to assess performance and compliance with regulatory requirements.
• Document testing results, defects and analysis outputs in accordance with established standards and procedures.
• Participate in user testing of new AFC analytics tools, models and systems upgrades.
• Gather feedback and contribute to improving system usability and effectiveness.
• Support project delivery across testing, implementation and post‑go‑live issue tracking, including coordination with stakeholders and timely resolution of system issues.
• Collaborate with technology team to support the enhancements and modernization of AFC Core Engine systems / platform, including initiatives leveraging AI/ML capabilities.
• Work with senior analysts and IT teams to troubleshoot and enhance AFC models & systems functionality.
• Support BAU monitoring and reporting activities to ensure continuity of operations and timely identification of potential issues or risks.

Skills

Analytical thinking

Critical thinking

Education

Bachelor's degree in Data Science, Statistics, Finance, Business Administration, or Computer Science

Tools

Excel

SQL

Python

Power BI

Qlik

Job description

Job Summary

We are seeking a detail-oriented and analytical professional to support Anti-Financial Crime (AFC) data analytics, surveillance model validation, testing, and system enhancements. This role plays an important part in ensuring the accuracy, efficiency, and regulatory compliance of AFC monitoring systems while supporting modernization initiatives, including AI/ML-enabled capabilities.

Mandatory Skill-set

• Bachelor's degree in Data Science, Statistics, Finance, Business Administration, Computer Science, or a related discipline;
• Must have minimum of 5 years' experience in the financial services industry;
• Must have hands‑on experience with AML systems, transaction monitoring, and Anti‑Financial Crime compliance;
• Experience in data analytics, reporting, and project support or coordination
• Proficiency in Microsoft Excel, SQL and Phyton;
• Experience with data visualization tools such as Power BI or Qlik;
• Strong analytical and critical thinking skills;

Desired Skill-set

• Practical knowledge of automation technologies (e.g., Robotic Process Automation (RPA);
• Experience in User Acceptance Testing (UAT), system testing, and defect management;
• Familiarity with AI/ML applications in compliance or financial crime detection.

Responsibilities

• Conduct data analysis to identify patterns and anomalies related to money laundering, fraud and sanctions;
• Assist in reviewing and refining alerts, reducing false positives, and improving detection efficiency;
• Support monitoring and validation of AFC surveillance models and Core Engine systems, ensuring data accuracy and performance tracking;
• Support data analytics activities including alert performance review, anomaly identification, and development or quality assurance of dashboards / reports;
• Perform data extraction, validation and analysis using SQL or relevant analytics tools where applicable;
• Support multi‑system AML testing across Core Engine platforms, primarily in user acceptance testing (UAT), including defect tracking and validation;
• Conduct regular testing and validation of AFC monitoring models to assess performance and compliance with regulatory requirements;
• Document testing results, defects and analysis outputs in accordance with established standards and procedures;
• Participate in user testing of new AFC analytics tools, models and systems upgrades;
• Gather feedback and contribute to improving system usability and effectiveness;
• Support project delivery across testing, implementation and post‑go‑live issue tracking, including coordination with stakeholders and timely resolution of system issues;
• Collaborate with technology team to support the enhancements and modernization of AFC Core Engine systems / platform, including initiatives leveraging AI/ML capabilities;
• Work with senior analysts and IT teams to troubleshoot and enhance AFC models & systems functionality;
• Support BAU monitoring and reporting activities to ensure continuity of operations and timely identification of potential issues or risks.

EA License No. 07C5639