This list contains only the countries for which job offers have been published in the selected language (e.g., in the French version, only job offers written in French are displayed, and in the English version, only those in English).
We’re looking for talented Data Scientist who can push the boundaries of our existing models and help design state-of-the-art solutions to GTM challenges that accelerate revenue growth and improve commercial efficiency. In this role, you’ll partner closely with Product, Growth, and Commercial teams to shape and build the next-generation data science foundation at Airwallex. This role is ideal for someone who takes meaningful ownership from day one—someone who digs deeply into data to understand why outcomes change (not just what changed), balancing analytical rigor with speed and business context—and then leverages AI to translate those insights into scalable models and solid data foundations.
Job Responsibility:
Translate complexity into action: Turn complex statistical and modeling results into clear, compelling, and actionable narratives for cross-functional partners and executive audiences
Uncover and scale revenue insights: Lead proactive, exploratory analyses to identify latent revenue levers, emerging trends, and root causes behind shifts in key GTM metrics—and operationalize these learnings into repeatable workflows, automated pipelines, and scalable data science operating models
Build revenue forecasting and performance insights: Develop and own revenue forecasting and forward-looking performance insights (e.g., pipeline health, conversion and retention drivers, scenario planning), providing a reliable “source of truth” that helps teams make faster, better commercial decisions
Apply advanced causal inference: Use advanced observational causal inference methods (e.g., DiD, synthetic control, DoubleML) to estimate impact and inform decisions when randomized experiments are infeasible
Embed AI into commercial workflows: Design and deploy AI-enabled solutions across the sales and customer lifecycle—enhancing sales calls and coaching, improving sales effectiveness, and generating proactive, transaction-based customer insights to drive retention and expansion
Requirements:
5+ years of industry experience and an advanced degree (MS or PhD) in a quantitative field (e.g., Statistics, Computer Science, Engineering, Economics, or a related discipline)
Strong analytical intuition and structured problem-solving
Excellent communicator and storyteller—able to translate technical work into crisp, actionable recommendations for both technical and non-technical stakeholders, including executives
Deep curiosity about GTM performance and customer behavior
Strong foundations in causal inference and forecasting, with experience applying methods such as DiD, synthetic control, and modern ML-based approaches to real business problems
High fluency in analytics tooling—strong SQL skills and proficiency in Python and/or R for analysis, modeling, and automation
Nice to have:
Experience with Databricks or similar cloud data platforms / warehouses
Familiarity with Hex or other notebook-based analysis tools
Experience in a high-growth startup and/or B2B business models (e.g., pipeline, CRM, RevOps data)