Data Science Director, Growth

Airwallex
US - San FranciscoPosted 24 March 2026

Job Description

Data Science Director, Growth 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 200,000 businesses worldwide – including Brex, Rippling, 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 2,000 of the brightest and most innovative people in tech across 26 offices around the globe. Valued at US$8 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 https://www.airwallex.com/us/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. THE ROLE We’re looking for a Data Science Director for Growth to lead the data science engine behind Airwallex’s next phase of hyper-growth. This is a senior leadership role at the intersection of data, product, and growth strategy — responsible for driving measurable impact across acquisition, activation, conversion, retention, and monetization. You will shape how we use experimentation, advanced analytics, and machine learning to unlock step-function growth, partnering closely with leaders in Product, Engineering, Marketing, and Design. This role is ideal for a growth-minded data science executive who thrives in a fast-scaling, high-impact environment. This is a full-time role based in San Francisco. WHAT YOU’LL DO - Lead & Scale a High-Impact Team: - Build, mentor, and inspire a world-class data science & AI team focused on driving user and revenue growth. - Foster expertise in applied ML and GenAI, ensuring the team balances experimentation, traditional DS rigor, and cutting-edge AI capabilities. - Own Experimentation at Scale: - Architect and oversee our experimentation framework — A/B testing, multi-arm bandits, and causal inference — to accelerate innovation. - Extend this framework into AI-driven adaptive experimentation, leveraging bandits and reinforcement learning to allocate growth investments dynamically. - Develop Growth-Focused AI Solutions: - Agentic workflow for Growth: build copilots and agent workflows (e.g., automated campaign generation, sales assistant bots, lead research copilots) - Personalization engines: Use embeddings and recommendation models to deliver contextualized product experiences and lifecycle messaging. - Advanced segmentation & profiling: - Learn patterns & turn data into numbers: Leverage ML + embeddings to identify high-value prospects and clusters. - Churn & retention modeling: Predict customer behavior and trigger AI-driven lifecycle interventions. - Propensity scoring & lead prioritization: Blend predictive ML with LLM-powered enrichment of firmographics/intent data. - Pricing & incentive optimization: Apply reinforcement learning and causal ML to optimize offers in real time. - Deliver Strategic Insights & AI- ... (truncated, view full listing at source)
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