Description
About the Role
As a Senior Data Scientist, you will tackle challenging risk problems across credit and fraud, 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
Requirements:
- 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
Benefits:
- 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