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Staff Data Scientist
Airwallex · UK - London
Publicada em 21/10/2026
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About AirwallexAirwallex 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. How you'll make impactAs 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 sys
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