Staff Data Scientist, Algorithm (Payments Optimization)
AirwallexSG - SingaporePosted 8 April 2026
Job Description
Staff Data Scientist, Algorithm (Payments Optimization)
ABOUT AIRWALLEX
Airwallex is the only unified payments and financial platform for global businesses. Powered by our unique combination of proprietary infrastructure and software, we empower over 200,000 businesses worldwide – including Brex, Rippling, Navan, Qantas, SHEIN and many more – with fully integrated solutions to manage everything from business accounts, payments, spend management and treasury, to embedded finance at a global scale.
Proudly founded in Melbourne, we have a team of over 2,000 of the brightest and most innovative people in tech across 26 offices around the globe. Valued at US$8 billion and backed by world-leading investors including T. Rowe Price, Visa, Mastercard, Robinhood Ventures, Sequoia, Salesforce Ventures, DST Global, and Lone Pine Capital, Airwallex is leading the charge in building the global payments and financial platform of the future. If you’re ready to do the most ambitious work of your career, join us.
ATTRIBUTES WE VALUE
We hire successful builders with founder-like energy who want real impact, accelerated learning, and true ownership. You bring strong role-related expertise and sharp thinking, and you’re motivated by our mission and operating principles https://www.airwallex.com/us/operating-principles. You move fast with good judgment, dig deep with curiosity, and make decisions from first principles, balancing speed and rigor.
You're humble and collaborative; turn zero‑to‑one ideas into real products, and you “get stuff done” end-to-end. You use AI to work smarter and solve problems faster. Here, you’ll tackle complex, high‑visibility problems with exceptional teammates and grow your career as we build the future of global banking. If that sounds like you, let’s build what’s next.
About the Role:
We are seeking a highly skilled and experienced Staff Data Scientist to drive the technical direction of our Payments Optimization team. This is a critical Individual Contributor (IC) role that will focus on designing and implementing advanced algorithms to optimize payments. You will act as a key strategic partner to Product, Engineering, and Commercial leaders, identifying complex, unique opportunities to improve payments performance and drive product adoption at scale.
Responsibilities:
- Define and drive the technical strategy for multi-objective payments optimization, ensuring that AWX optimizes payment conversion rates, fraud risk, and transaction costs.
- Act as the strong technical owner on cross-functional initiatives, solving unique, critical, and complex problems that have a broad impact on the business.
- Provide technical thought leadership on various optimization algorithms (e.g. contextual bandits) and experimentation techniques (e.g. A/B testing, causal inference).
- Collaborate with engineering teams to design and build tools and infrastructure to enable optimization at scale and deploy ML systems to production.
- Partner with Product, Engineering, Risk, and other cross-functional teams to inform and influence product strategy and investment decisions.
- Clearly and persuasively communicate complex data insights and technical recommendations to both technical and non-technical audiences, ensuring your findings influence stakeholders at all levels.
- Mentor and coach junior data scientists and engineers.
Qualifications:
- Advanced degree (PhD or MS) in a quantitative field such as Statistics, Engineering, Computer Science, Economics, or related disciplines.
- 5+ years of industry experience in data science or applied ML.
- Proven experience in experimentation and causal inference to drive data-informed product decisions with concrete impact.
- Strong experience in deploying ML systems to production is a big plus.
- Demonstrated ability to operate as a technical leader, defining and developing critical systems and influencing product strategy in high-growth environments.
- Proficient in SQL and Python ... (truncated, view full listing at source)
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