Data Scientist Fraud & AML Analytics

Experience: 5-7 years

Key Responsibilities

• Analyse large transaction, banking, customer, vendor and financial datasets for fraud and AML indicators.
• Identify suspicious patterns, anomalies, mule accounts, unusual fund movements and related-party linkages.
• Perform fund-flow, money-trail and transaction chronology analysis.
• Analyse bank statements, invoices, vendor masters, payment records and approval trails.
• Use Python, SQL, Excel and Power Query for data cleaning, reconciliation and analytics.
• Develop dashboards, exception reports, relationship maps and investigation outputs.
• Support AML, fraud and forensic investigations with evidence-backed analytical findings.
• Identify data gaps, inconsistencies and areas requiring further investigation.
• Maintain clear, source-referenced working papers and methodology.
• Use AI tools for investigation analytics and triage, with proper human validation.

Key Skills

• Forensic / AML / Fraud Analytics
• Transaction & Financial Crime Analysis
• Python, SQL, Excel, Power Query
• Fund Flow & Money Trail Analysis
• Anomaly / Pattern Detection
• BFSI / Banking Data
• AI-assisted Investigation Analytics

Preferred: CAMS, CFE, CFCS or relevant Data Analytics certification.