Staff Data Scientist, Algorithm, Growth
Airwallex is hiring a Staff Data Scientist, Algorithm, Growth in Seattle, United States. It pays $250k-$299k a year and Level rates it ; you can apply on Level.
AI in this role
Build predictive models, personalization systems, and LLM-powered applications to drive global customer growth at Airwallex.
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 2,300 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 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.
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’ll be based in Seattle and work from the office three days a week.
What we're looking for
Essentials
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.
Preferred
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.
You'll thrive here if
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.
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.
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.
How we rate this
Staff Data Scientist, Algorithm, Growth at Airwallex rates 85 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in data scientist jobs at this level
Pay
- This job starts at
- $250k
- Typical data scientist job
- $168k
This posting starts 49% above the typical data scientist job. See data scientist jobs
Questions you could be asked
- How do you decide when an AI agent can act on its own versus asking for approval first?
- Tell me about a project where machine learning was part of your work. What did you do?
- Tell me about a project where predictive modeling was part of your work. What did you do?
- Tell me about a project where recommendation systems was part of your work. What did you do?
- Tell me about a project where causal inference was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: AI agents, Machine learning, Predictive Modeling, Recommendation Systems, and Causal Inference. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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