Senior Data Scientist, Algorithm (Lending & Onboarding)

About the team

The risk platform team at Airwallex is responsible for managing risk across all Airwallex products, including topics like GTPN, Payments, Issuing, Onboarding, Account Takeover, Lending, and Credit. The risk landscape is constantly changing, and threats are becoming increasingly sophisticated. We are at the forefront of innovation in risk management.

What you’ll do

As a Senior Data Scientist, you will tackle challenging risk problems across credit and fraud (e.g., account takeover), and help build data-driven decision systems that deliver meaningful business impact. You will be responsible for translating business problems into analytical frameworks, strategies, models, and scalable data solutions.

Responsibilities:

Build real-time ML models to protect Airwallex customers and platform assets, such as Account Takeover prevention and fraud decisioning

Develop credit risk models and analytical frameworks to assess merchant risk and support the growth of Airwallex’s lending business

Translate business requirements into analytical, modeling, and strategy problems across fraud, credit, onboarding, and related risk domains

Design performance monitoring, metrics frameworks, and decision-support tools to improve risk visibility and decision quality

Partner with Product, Engineering, Operations, and other cross-functional teams to turn data, models, and insights into production strategies and measurable business impact

Who You Are

We're looking for people who meet the minimum qualifications for this role. The preferred qualifications are great to have, but are not mandatory.

Minimum qualifications:

3+ years of experience in analytics, data science, risk strategy, or a related quantitative role

Bachelor’s degree or above in a quantitative field such as Statistics, Mathematics, Economics, Computer Science, Engineering, or a related discipline

Proficient in Python and SQL

Strong problem-solving skills and experience working with large datasets

Experience partnering with cross-functional stakeholders to solve business problems

Preferred qualifications:

Experience in credit, fraud, or related risk domains

Hands-on experience in model development, validation, or monitoring

Experience designing risk rules, decision strategies, or performance monitoring frameworks

Why Join Us:

Opportunity to work on impactful problems that directly influence payments success and business outcomes.

Collaborate with a talented, cross-functional team in a fast-paced, high-growth environment.

Drive the adoption of advanced data science techniques and experimentation to improve customer and business outcomes.

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 email address. Please apply only through 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.