Airwallex is looking to lead the data science engine behind its next phase of hyper-growth by driving measurable impact across acquisition, activation, conversion, retention, and monetization.
Requirements
- Strong background in analytics, statistical modeling, causal inference, experimentation design, and proficiency with Python/R, SQL, and cloud-based data platforms.
Responsibilities
- 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.
- 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.
- 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.
- Pricing & incentive optimization: Apply reinforcement learning and causal ML to optimize offers in real time.
Other
- 10+ years in data science, analytics, or applied ML, including 5+ years leading teams at scale in top-tier tech, fintech, or growth-driven businesses.
- Proven track record of using data science to drive customer acquisition, funnel optimization, personalization, and retention in high-growth B2B products.
- Comfortable navigating business trade-offs, prioritizing initiatives for outsized growth impact.
- Skilled at partnering with Product, Engineering, and Marketing leaders to align on shared goals.
- Thrives in fast-scaling environments with high ambiguity and is excited to build teams, systems, and best practices from the ground up.
- Exceptional verbal and written communication skills with the ability to influence executive-level stakeholders and translate complex model results into actionable business insights.
