SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Airwallex is a unified payments and financial platform serving over 250,000 global businesses including Brex, Navan, Qantas, and SHEIN. The company operates across 27 offices worldwide with 2,300+ employees and is valued at $11 billion, backed by leading investors including T. Rowe Price, Visa, Mastercard, and Sequoia.
As a Staff Data Scientist in the Risk Product Data Science team, you will be a technical leader responsible for advancing how Airwallex proactively identifies and mitigates financial crime risk. You will work across customer, transaction, behavioral, network, and external intelligence data to detect known and emerging risks, uncover suspicious communities and coordinated activity, and improve customer and counterparty screening for financial crime exposure.
Key responsibilities include leading the Data Science strategy for AML and Financial Crime risk controls, developing proactive risk detection capabilities to uncover emerging threats and suspicious behaviors, and scaling advanced risk capabilities across AI-powered screening, graph and network intelligence, entity resolution, and machine learning. You will advance key AML/FCC use cases including name screening, transaction monitoring, and detection of coordinated activity and fraud rings. You'll partner closely with Risk Product, Engineering, Financial Crime Compliance, and Risk Operations to translate complex risk problems into scalable, production-grade controls, while influencing the Risk Product roadmap and raising the technical bar through technical leadership and mentoring.
Required: 7+ years in Data Science, Machine Learning, Applied AI, Risk Analytics, or related quantitative field with demonstrated Staff-level impact. Strong expertise in applied machine learning and modern AI with experience productionizing data-driven solutions at scale. Experience with NLP/LLMs, entity resolution, information retrieval, graph/network analytics, anomaly detection, or risk scoring. Must have strong SQL and Python skills and excellent communication and stakeholder management abilities.
Preferred: fintech, payments, banking, or regulated domain experience; AML, financial crime, sanctions, PEP screening, or fraud background; experience building name screening or adverse media systems; familiarity with LLM evaluation and AI observability; graph analytics experience for detecting mule networks and coordinated activity; proactive risk discovery capabilities.
