Data Scientist, GTM

Airwallex • San Francisco, CA

5d

About The Position

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.
We’re looking for talented candidates 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.

Requirements

• 3+ 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—you ask the right questions, explore data thoughtfully, and synthesize clear, defensible conclusions.
• 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—you go beyond “what happened” to understand “why it happened,” while staying pragmatic and focused on impact.
• 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 Haves

• 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).

Responsibilities

• 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level