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

ABOUT AIRWALLEX

Airwallex is the AI-native financial operating system for a real-time, intelligent economy. More than 676,000 businesses, including McLaren Racing, Qantas, SHEIN, and TikTok, use us, directly or through our platform partners, to run their financial operations or build and monetize financial products of their own.

We started in Melbourne in 2015 to build the infrastructure global commerce runs on. We're the regulated backbone behind global payments: not by accident, but by design. A decade plus, 85+ licenses, and a financial infrastructure spanning North America, Europe, the Middle East, and Asia-Pacific.

We're co-headquartered in San Francisco and Singapore, with more than 2,300 people across 27 offices. We hire builders with founder-level energy, people who move fast with good judgment, dig in with real curiosity, and make calls from first principles rather than waiting to be told what to do. Read our operating principles https://www.airwallex.com/en-us/operating-principles to see it in full.

WHAT YOU’LL DO

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 how we screen customers and counterparties for financial crime exposure.

A key part of the role is developing AI-powered risk detection capabilities, including name screening, entity resolution, network intelligence, and proactive discovery of previously unknown risk patterns.

You will partner closely with Risk Product, Engineering, Financial Crime Compliance and Risk Operations to translate complex risk problems into scalable, production-grade controls.

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.

- Develop and scale advanced risk capabilities across AI-powered screening, graph and network intelligence, entity resolution, and machine learning.

- Advance key AML/FCC use cases including name screening, transaction monitoring, and detection of coordinated activity and fraud or financial crime rings.

- Partner with Risk Product, Engineering, Financial Crime Compliance and Risk Operations to translate complex risk problems into effective, scalable production controls.

- Influence the Risk Product roadmap and raise the technical bar for Risk Data Science through technical leadership, reusable frameworks, and mentoring.

MINIMUM REQUIREMENTS

- 7+ years of experience in Data Science, Machine Learning, Applied AI, Risk Analytics, or a related quantitative field, with demonstrated impact at Staff, Lead, or equivalent scope.

- Strong expertise in applied machine learning and modern AI, with experience building and productionizing data-driven solutions at scale.

- Experience with one or more areas highly relevant to financial crime detection, such as NLP/LLMs, entity resolution, information retrieval, graph or network analytics, anomaly detection, or risk scoring.

- Strong SQL and Python skills are must

- Strong communication and stakeholder management skills, with the ability to influence senior partners across Product, Engineering, Risk, Operations, and Compliance.

PREFERRED QUALIFICATIONS

- Experience in fintech, payments, banking, marketplaces or another regulated or high-risk domain.

- Experience in AML, financial crime, sanctions, PEP screening, adverse media, fraud, trust and safety or transaction monitoring.

- Experience building or improving name screening or adverse media systems.

- Familiarity with modern LLM evaluation, prompt/model experimentation, retrieval quality assessment and AI observability.

- Experience applying graph analytics to detect mule networks, collusive behavior, coordinated account activity or interconnected high-risk entities.

- Experience developing proactive risk discovery capabilities rather than exclusively optimizing existing supervised models or rules.

APPLICANT SAFETY POLICY: FRAUD AND THIRD-PARTY RECRUITERS

To protect you from recruitment scams, please be aware that Airwallex will not ask for bank details, sensitive ID numbers (i.e. passport), or any form of payment during the application or interview process. All official communication will come from an @airwallex.com http://airwallex.com email address. Please apply only through careers.airwallex.com http://careers.airwallex.com or our official LinkedIn page.

Airwallex does not accept unsolicited resumes from search firms/recruiters. Airwallex will not pay any fees to search firms/recruiters if a candidate is submitted by a search firm/recruiter unless an agreement has been entered into with respect to specific open position(s). Search firms/recruiters submitting resumes to Airwallex on an unsolicited basis shall be deemed to accept this condition, regardless of any other provision to the contrary.

EQUAL OPPORTUNITY

Airwallex is proud to be an equal opportunity employer. We value diversity and anyone seeking employment at Airwallex is considered based on merit, qualifications, competence and talent. We don’t regard color, religion, race, national origin, sexual orientation, ancestry, citizenship, sex, marital or family status, disability, gender, or any other legally protected status when making our hiring decisions. If you have a disability or special need that requires accommodation, please let us know.