Staff Data Scientist, Algorithm (Payments Fraud & Optimization)

About the role

Airwallex, the AI-native financial operating system for global commerce, is looking for a Staff Data Scientist to lead its Payments Optimization team. This is a critical individual contributor role focused on designing and implementing advanced algorithms to optimize payments, acting as a strategic partner to Product, Engineering and Commercial leaders.

Key responsibilities

• Define and drive the technical strategy for multi-objective payments optimization, balancing payment conversion rates, fraud risk and transaction costs.
• Act as technical owner on cross-functional initiatives, solving complex problems with broad business impact.
• Provide thought leadership on optimization algorithms (e.g. contextual bandits) and experimentation techniques (e.g. A/B testing, causal inference).
• Work with engineering teams to build tools and infrastructure for optimization at scale and deploy ML systems to production.
• Partner with Product, Engineering, Risk and others to inform product strategy and investment decisions.
• Communicate complex data insights and technical recommendations clearly to technical and non-technical audiences.
• Mentor and coach junior data scientists and engineers.

Required profile

• Advanced degree (PhD or MS) in a quantitative field such as Statistics, Engineering, Computer Science, Economics or a related discipline.
• 5+ years of industry experience in data science or applied machine learning.
• Proven experience in experimentation and causal inference with measurable impact.
• Demonstrated ability to act as a technical leader in high-growth environments.

Required skills

• Proficiency in SQL and Python/R.
• Experience deploying ML systems to production is a big plus.
• Experience using LLMs, AI and agents to improve processes and ML models.

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 and does not accept unsolicited resumes from recruiters.