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. 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. About the team The Growth Analytics team at Airwallex is a dynamic group of data scientists and analytics professionals. We are passionate about leveraging data to drive business growth, optimize strategies, and unlock new opportunities across the company. As key partners to Growth, Marketing, Sales, and Product, our team works collaboratively to enable data-informed decision-making, experimentation, and scalable measurement frameworks supporting Airwallex's global expansion. Learn more about the data science team in this blog What you'll do - Partner closely with Growth, Marketing, Sales, and Product teams to derive actionable data-driven insights and impact business outcomes - Analyze data to assess performance and ROI of marketing and growth initiatives - Develop dashboards and robust, scalable measurement frameworks to empower data-driven decision-making - Identify trends and improvement opportunities to sharpen growth and marketing strategies - Act as the subject matter expert for marketing KPIs and analytics best practices; consult on marketing data projects - Design and conduct experiments to measure the impact of ad spend, brand campaigns, and other initiatives - Translate open-ended or complex business challenges into high-impact, structured analytics projects - Communicate analysis results clearly to both technical and non-technical audiences, influencing strategy and decisions Who you are - At least 6 years of industry experience and an advanced degree (PhD or MS) in a quantitative field (e.g. Statistics, Engineering, Sciences, Computer Science, Economics) - 2+ years of experience in Marketing Analytics or a similar role - Proven experience presenting actionable, data-driven recommendations to executives and cross-functional teams - Strong technical proficiency with SQL, Python, and/or R; experience in dimensional data modeling and schema design is a plus - Experience with digital marketing channels such as email, paid media, and SEO highly advantageous - Excellent communication and interpersonal skills in high-growth, cross-functional environments - Experience in technology, financial services, or high-growth organizations is advantageous Preferred qualifications: - Experience mentoring or leading analytics or data science teams in fast-paced or fintech environments - Familiarity with Google Analytics, Marketo, Salesforce, and A/B testing frameworks - Ability to perform advanced statistical analysis and develop machine learning models 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. #BI-Hybrid

• Design, develop, and deploy machine learning models, including LLMs, transformer-based models, and traditional ML approaches • Build and optimize NLP, recommendation systems, and predictive analytics solutions to address complex business problems • Develop and implement RAG (Retrieval-Augmented Generation), prompt engineering strategies, and emerging agentic AI workflows • Conduct rigorous model evaluation using appropriate statistical methods and performance metrics • Acquire, integrate, and preprocess structured and unstructured data from diverse sources (e.g., relational databases, NoSQL systems, logs, external datasets) • Perform feature engineering and data preparation to ensure high-quality inputs for ML models • Ensure data quality, validation, and governance compliance for AI/ML use cases • Design and maintain scalable data and ML pipelines to support model training, evaluation, deployment, and monitoring • Deploy models into production using cloud-native architectures and APIs • Implement model monitoring, drift detection, and retraining strategies to ensure sustained performance • Contribute to CI/CD pipelines and ML lifecycle automation • Contribute to system architecture decisions across data, ML, and cloud platforms • Provide technical leadership in selecting tools, frameworks, and design patterns for AI/ML solutions • Mentor junior team members and promote best practices in data science and ML engineering • Partner with stakeholders, product owners, and subject matter experts to translate business needs into AI/ML solutions • Communicate findings, insights, and recommendations clearly to both technical and non-technical audiences • Deliver solutions that drive measurable improvements in efficiency, quality, and business outcomes.

• Define and implement a multi-year enterprise data strategy aligned to business, clinical, and technology objectives. • Serve as the executive owner for data platforms, data governance, metadata strategy, and enterprise data quality. • Oversee modern, scalable data platform architecture (e.g., ingestion, transformation, consumption layers). • Modernize workloads using platforms such as Databricks and improve data orchestration, monitoring, observability, and automation. • Ensure standardized, business-ready “gold‑layer” datasets to support analytics and data science. • Consolidate enterprise reporting under a unified model with consistent definitions, governed metrics, and repeatable processes. • Partner with business leaders to deliver actionable analytics insights tied to operational, clinical, and financial KPIs. • Maximize performance, consistency, and usability of dashboards and analytical assets. • Establish and lead a data governance framework covering quality, lineage, security, and stewardship. • Ensure compliance with regulatory, privacy, and security standards, including evolving AI-related governance expectations. • Provide platforms, standards, and data quality foundations required for scalable, trustworthy machine‑learning and AI adoption. • Partner with Data Science leadership to ensure strong alignment on data needs and lifecycle processes. • Build and develop a high-performing team across data engineering, analytics, governance, and architecture. • Foster a culture of accountability, innovation, and continuous improvement. • Provide clear operating models for how data teams partner with technology and business units.

• Work with partner ML and Annotation engineers and TPMs to specify data and training requirements • Ensure that datasets are correctly curated, filtered, and processed to meet the rigorous standards of production-level machine learning • Act as a technical translator, turning high-level project goals into actionable data specifications for engineering and annotation teams • Maintain high standards for data integrity, ensuring that the media datasets used for training are representative and accurate