Senior Data Scientist, Algorithm (Lending & Onboarding)

About the role

Airwallex's risk platform team manages risk across its products, including GTPN, Payments, Issuing, Onboarding, Account Takeover, Lending and Credit. As a Senior Data Scientist, you will tackle challenging risk problems in credit and fraud (such as account takeover) and help build data-driven decision systems. You will translate business problems into analytical frameworks, strategies, models and scalable data solutions.

Key responsibilities

• Build real-time ML models to protect 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 the 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 teams to turn data, models and insights into production strategies and measurable business impact

Required profile

• 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
• Strong problem-solving skills and experience working with large datasets
• Experience partnering with cross-functional stakeholders to solve business problems

Required skills

• Proficiency in Python and SQL
• Preferred: hands-on experience in model development, validation or monitoring
• Preferred: experience designing risk rules, decision strategies or performance monitoring frameworks

The role is full-time. Applicants should apply through the company's careers site; the company states it will not ask for bank details or sensitive IDs during the application or interview process.