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Staff Data Scientist, Algorithm (Payments Fraud & Optimization)

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Onsite

Staff Data Scientist, Algorithm (Payments Fraud & Optimization)

Airwallex

Staff Data Scientist, Algorithm (Payments Fraud & Optimization)

Skills

SQL QueriesPythonProduct StrategyData ScienceRMachine LearningAI

2 months ago

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Important information


Contract type:

Permanent

Location:

Singapore

Work mode:

Onsite

Published on:

7 September 2026

What they need


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 250,000 businesses worldwide – including Brex, 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 2,300 of the brightest and most innovative people in tech across 27 offices around the globe. Valued at US$11 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. 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/R.
  • Experience using LLM, AI and Agent to improve processes and ML models

Why Join Us

  • 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.

Applicant Safety Policy: Fraud and Third-Party Recruiters To protect you from recruitment scams, please be aware that Airwallex will not ask for bank details, sensitive ID numbers (i.e. passport), or any form of payment during the application or interview process. All official communication will come from an @ airwallex.com email address. Please apply only through careers.airwallex.com or our official LinkedIn page. Airwallex does not accept unsolicited resumes from search firms/recruiters. Airwallex will not pay any fees to search firms/recruiters if a candidate is submitted by a search firm/recruiter unless an agreement has been entered into with respect to specific open position(s). Search firms/recruiters submitting resumes to Airwallex on an unsolicited basis shall be deemed to accept this condition, regardless of any other provision to the contrary. Equal opportunity Airwallex is proud to be an equal opportunity employer. We value diversity and anyone seeking employment at Airwallex is considered based on merit, qualifications, competence and talent. We don’t regard color, religion, race, national origin, sexual orientation, ancestry, citizenship, sex, marital or family status, disability, gender, or any other legally protected status when making our hiring decisions. If you have a disability or special need that requires accommodation, please let us know.

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