Staff Data Scientist

Airwallex is the AI-native financial operating system for a real-time, intelligent economy. More than 675,000 businesses, including McLaren Racing, Qantas, SHEIN, and TikTok, use us, directly or through our platform partners, to run their financial operations or build and monetize financial products of their own.

We started in Melbourne in 2015 to build the infrastructure global commerce runs on. We're the regulated backbone behind global payments: not by accident, but by design. A decade plus, 85+ licenses, and a financial infrastructure spanning North America, Europe, the Middle East, and Asia-Pacific.

We're co-headquartered in San Francisco and Singapore, with more than 2,300 people across 27 offices. We hire builders with founder-level energy, people who move fast with good judgment, dig in with real curiosity, and make calls from first principles rather than waiting to be told what to do. Read our operating principles to see it in full.

As a Staff Data Scientist, you'll help Airwallex make better strategic and operational decisions through rigorous analysis, forecasting, and clear data storytelling. You'll combine hands-on analytical leadership with strategic influence, helping evolve data science into a more scalable, general capability across the business.

• Uplevel the data science team through technical mentorship, hands-on leadership on complex analytical problems, and a clear vision for the team’s longer-term direction.

• Act as a strategic thought partner to cross-functional teams, using data and technical expertise to inform, influence, and execute strategy and investment decisions.

• Apply quantitative analysis, data mining, forecasting, and clear data storytelling to answer important business questions and guide strategic decisions.

• Define goals, build forecasts, and monitor key business metrics to identify trends and assess performance across the organisation.

• Help evolve data science from individual analyses into scalable systems of decision support, establishing robust analytical approaches that produce reliable insights repeatedly.

You'll work from our London office three days a week.

• 8+ years of industry experience.

• An advanced degree, such as a PhD or MS, in a quantitative field such as Statistics, Engineering, Sciences, Computer Science, or Economics.

• Experience communicating analytical results to leadership and cross-functional teams to influence strategy.

• Experience providing technical mentorship in data science or quantitative analysis in an industry setting.

• Expert-level ability in data-querying languages such as SQL, scripting languages such as Python, and/or statistical or mathematical software such as R.

• You're comfortable owning the roadmap yourself.

• You own the outcome and you don't wait for permission to fix what's broken.

• You're comfortable with ambiguity. Give you a problem, not a prescription, and you'll run with it.

• You connect dots across functions and hold the big picture without losing grip on the details.

• You simplify complexity rather than hiding behind it.

Data Science at Airwallex is evolving from individual analyses and standalone models into scalable systems of decision and action. We bring together two complementary capabilities: DS Analytics, which improves decision quality and reduces decision latency, and DS Algorithm, which directly optimizes product and business outcomes in real time.

You’ll build systems of decision that help teams understand what changed, determine what to do next, and learn whether the action worked. This means owning the AI analytics harness around the analysis—including semantic layers, context retrieval, evaluation infrastructure, validation gates, and governance—so decisions remain reliable, contextualized, and trustworthy at scale.

You’ll also build systems of action that use machine learning and AI to shape product behavior, ranking, pricing, risk, allocation, and customer experiences. The work extends beyond training models to designing how intelligent systems reason, use tools, operate within guardrails, execute workflows, and measure their incremental impact.

As AI makes ad hoc analysis increasingly accessible, the remaining advantage is scale, scientific method, and rigor. You’ll structure ambiguous problems, design experiments, identify confounding factors, challenge misleading conclusions, and build production systems that produce reliable outcomes repeatedly—not just one-off answers.

The work is moving up the stack: from analysis to system, from model to harness, and from answering a question once to building something that answers it correctly over time. If you want to create data science systems that compound decision quality and turn intelligence into measurable action, this is where that work happens.

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.

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.