Staff Data Scientist, Algorithm (Risk Product - AML & Financial Crime)

About the role

Airwallex, the AI-native financial operating system used by more than 675,000 businesses, is hiring a Staff Data Scientist in its Risk Product Data Science team in Singapore. You will be a technical leader responsible for advancing how Airwallex proactively identifies and mitigates financial crime risk, working across customer, transaction, behavioral, network and external intelligence data.

Key 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, control gaps and previously unknown financial crime patterns.
• Build and scale AI-powered capabilities in screening, graph and network intelligence, entity resolution and machine learning.
• Advance use cases such as name screening, transaction monitoring and detection of coordinated activity and fraud rings.
• Partner with Risk Product, Engineering, Financial Crime Compliance and Risk Operations to turn complex risk problems into scalable production controls.
• Influence the Risk Product roadmap and raise the technical bar through reusable frameworks and mentoring.

Required profile

• 7+ years of experience in Data Science, Machine Learning, Applied AI, Risk Analytics or a related quantitative field, with Staff, Lead or equivalent scope.
• Experience in one or more areas relevant to financial crime detection, such as NLP/LLMs, entity resolution, information retrieval, graph or network analytics, anomaly detection or risk scoring.
• Preferred: fintech, payments, banking or regulated domain experience; AML, sanctions, PEP screening, adverse media, fraud, trust and safety or transaction monitoring experience; experience building name screening or adverse media systems.
• Preferred: familiarity with LLM evaluation, prompt and model experimentation, retrieval quality assessment and AI observability; experience using graph analytics to detect mule networks and coordinated account activity; experience building proactive risk discovery rather than only optimizing supervised models or rules.

Required skills

• Strong expertise in applied machine learning and modern AI, with experience building and productionizing data-driven solutions at scale.
• Strong SQL and Python skills are a must.
• Strong communication and stakeholder management skills, able to influence senior partners across Product, Engineering, Risk, Operations and Compliance.