Staff Data Scientist, AML Risk & Graph Intelligence

Airwallex; Singapore; On-site; SGD 180,000 - 260,000; Full time

Job summary

Airwallex Singapore is hiring a Staff Data Scientist to lead AML and financial crime risk across Risk Product Data Science. You will work with data from customers, transactions, and networks to detect emerging threats and improve screening of counterparties.

The role emphasizes AI-powered risk detection, including name screening and graph analytics, and requires collaboration with Risk Product, Engineering, Compliance, and Risk Operations to deliver scalable controls.

Qualifications

• 7+ years in DS/ML/Risk Analytics with staff-level impact.
• Strong expertise in applied ML and production-ready solutions at scale.
• Experience in NLP/LLMs, entity resolution, graph analytics, or risk scoring.
• Strong SQL and Python skills.
• Excellent communication and stakeholder management.

Responsibilities

• Lead the Data Science strategy for AML and Financial Crime risk controls across Risk Product.
• Develop proactive risk detection to uncover emerging threats, suspicious behaviors, and unknown patterns.
• Develop and scale advanced risk capabilities across AI-powered screening, graph and network intelligence, entity resolution, and ML.
• Advance AML/FCC use cases including name screening, transaction monitoring, and detection of coordinated activity and fraud rings.
• Partner with Risk Product, Engineering, Financial Crime Compliance and Risk Operations to translate complex risk problems into scalable production controls.
• Influence the Risk Product roadmap and raise the technical bar for Risk DS through reusable frameworks and mentoring.

Skills

SQL

Python

Machine learning

Stakeholder mgmt

Job description

Airwallex Singapore is hiring a Staff Data Scientist to lead AML and financial crime risk across Risk Product Data Science. You will work with data from customers, transactions, and networks to detect emerging threats and improve screening of counterparties.

The role emphasizes AI-powered risk detection, including name screening and graph analytics, and requires collaboration with Risk Product, Engineering, Compliance, and Risk Operations to deliver scalable controls.