Role Overview :

We are looking for a Data Scientist with strong experience in Fraud, AML, and financial crime analytics to analyze complex financial datasets and identify suspicious patterns, anomalies, and potential fraud indicators. The role will support fraud, AML, and forensic investigations through data-driven analysis, transaction intelligence, and evidence-backed insights.

Key Responsibilities :

- Analyze large transaction, banking, customer, vendor, and financial datasets to identify fraud and AML indicators.

- Identify suspicious transaction patterns, anomalies, mule accounts, unusual fund movements, and related-party relationships.

- Perform fund-flow, money-trail, transaction chronology, and financial relationship analysis.

- Analyze bank statements, invoices, vendor master data, payment records, approval trails, and other financial records.

- Clean, transform, reconcile, and analyze complex datasets using Python, SQL, Excel, and Power Query.

- Develop analytical dashboards, exception reports, investigation summaries, relationship maps, and other data-driven outputs.

- Support fraud, AML, and forensic investigations by translating complex data into clear, evidence-backed findings.

- Identify data gaps, inconsistencies, anomalies, and areas requiring further investigation.

- Develop analytical approaches and methodologies for transaction monitoring, anomaly detection, and investigative triage.

- Maintain clear, source-referenced working papers, analytical documentation, and methodology records.

- Leverage AI tools to improve investigation analytics, pattern identification, and case triage while ensuring appropriate human validation.

- Collaborate with investigation, compliance, finance, risk, and business teams to communicate findings and support decision-making.

Required Skills & Experience :

- 5 - 8 years of experience in Data Science, Fraud Analytics, AML Analytics, Forensic Analytics, or a related field.

- Strong hands-on experience analyzing transaction and financial crime datasets.

- Good understanding of fraud typologies, AML indicators, suspicious transactions, fund flows, and money trails.

- Strong proficiency in Python and SQL.

- Advanced Excel and Power Query skills.

- Experience with anomaly detection, pattern analysis, data reconciliation, and investigative analytics.

- Experience working with banking, BFSI, payments, financial services, or related datasets.

- Strong analytical and problem-solving skills with the ability to derive actionable insights from complex data.

- Strong documentation and communication skills, with the ability to present analytical findings clearly to business and investigation stakeholders.

- Exposure to AI-assisted analytics or investigation tools is preferred.