Senior Data Scientist, Analytics (Lending)

About the role

Airwallex's risk platform team manages risk across its products, including lending, payments, issuing and onboarding. As a Credit Risk Analyst - Lending, you will help monitor and optimise the performance of the business lending portfolio through data-driven analysis, portfolio insights and credit risk strategy support, turning portfolio and policy questions into actionable recommendations.

Key responsibilities

• Monitor and analyse existing lending portfolios, including delinquency trends, early warning indicators and credit quality metrics
• Identify emerging risks and provide insights to mitigate potential losses
• Support assessment and optimisation of portfolio-level credit strategies aligned with regional credit risk policies
• Prepare portfolio reports, risk dashboards and management summaries for senior leadership
• Work with Operations and Product teams to ensure consistent application of credit policies and risk controls
• Take part in portfolio reviews, stress testing and credit risk assessments
• Provide analytical support for regulatory reporting, audits and internal governance

Required profile

• 3+ years of experience in credit risk analytics, risk strategy, portfolio analytics or a related quantitative role
• Bachelor's degree or above in Quantitative Finance, Statistics, Computer Science, Business Analytics, Data Science or a related quantitative discipline
• Strong problem-solving skills and experience with large datasets
• Attention to detail and ability to spot data discrepancies
• Experience partnering with cross-functional stakeholders

Required skills

• Proficiency in SQL and Python
• Preferred: hybrid risk analytics and modelling experience, SME lending experience, ECL modelling, credit strategy analysis, credit policy design, stress testing and regulatory reporting familiarity

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 process.