Staff Data Scientist, Algorithm, Growth

About Airwallex

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 2,700 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 impact

You’ll shape the next phase of Airwallex’s growth by building data science systems that improve how we acquire, activate, engage, retain, and expand customers globally.

You’ll be based in Seattle and work from the office three days a week.

Learn more about your team

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.

Responsibilities

Design, deploy, and iterate on predictive models, personalization and recommendation systems, LLM-powered applications, incentive optimization frameworks, and causal inference models that drive measurable business outcomes.; Partner with Growth, Marketing, Product, Sales, Engineering, and other cross-functional teams to turn data and AI into clear recommendations, stronger investment decisions, and effective go-to-market strategies.; Measure the causal impact of marketing and growth initiatives, identify what’s working, and translate those findings into action.; Raise the technical bar for the data science team through hands-on leadership, mentorship, and a clear vision for longer-term direction.; 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 making decisions with incomplete information and moving quickly anyway.; You're not precious about your ideas. You push hard for what you believe in, but you pivot fast when the data says otherwise.; You connect dots across functions and hold the big picture without losing grip on the details.

Requirements

7+ years of industry experience and an advanced degree, such as a PhD or MS, in a quantitative field such as Statistics, Engineering, the Sciences, Computer Science, or Economics.; Expert knowledge of machine learning and statistical modelling for data-driven growth strategy.; Hands-on experience building, deploying, and maintaining machine learning models in production-grade systems, including cloud-based services and real-time or batch pipelines.; Strong experience with data querying languages such as SQL and scripting languages such as Python.; Experience applying machine learning and statistical models to measure the causal impact of marketing or growth initiatives.; Strong experience in AI harness and building AI agents.

Nice to have

Experience with schema design and dimensional data modelling.; Experience communicating analysis and recommendations to leadership and cross-functional teams to influence strategy.; Experience in technology, financial services, or a high-growth environment.; Experience providing technical mentorship on machine learning and experimentation in an industry setting.