• Design, build, and deploy machine learning models for real-time fraud detection and risk scoring. • Automate the monitoring of fraud and risk metrics using AI-driven alert systems. • Develop automated agents and systems to investigate and respond to suspicious activity, minimizing manual intervention. • Lead the research and implementation of cutting-edge AI and machine learning techniques for fraud prevention. • Conduct deep analysis of complex fraud patterns using advanced statistical and machine learning techniques. • Collaborate with engineering and product teams to integrate fraud prevention logic directly into our products. • Continuously optimize our models and detection strategies based on performance data. • Own the development and reporting of KPIs that measure the effectiveness of our AI-based fraud systems.

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 Strategic Data Science team provides Airwallex's Leadership with the data, tooling, and insights needed to amplify Airwallex's rapid growth. We partner across the entire business, giving us an overarching view and opinion on what Airwallex should prioritize. Our expertise spans the full data stack, and we use it to tackle complex data problems that will shape the future of global fintech. The team is growing quickly, and currently based in the US, China, and Singapore. Learn more about the data science team in this blog What you'll do Responsibilities: - Be the go-to data partner for regional executives - Use causal inference methods to understand how macroeconomic events impact the business. - Leverage AI to generate insights and improve business and team efficiency. - Lead Airwallex's forecasting efforts by 1) building tools that aggregate and evaluate forecasts 2) applying advanced statistical methods to build your own forecasts. - Scope and build data products. In some cases building MVPs, and in others, fully fleshed out solutions. - Mentor junior team members and cultivate an environment grounded in technical excellence, clear communication, and proactive critical thinking. Who you are We're looking for people who meet the minimum qualifications for this role. The preferred qualifications are great to have, but are not mandatory. Minimum qualifications: - 7+ years industry experience and an advanced degree (PhD or MS) in a quantitative field (e.g. Statistics, Engineering, Sciences, Computer Science, Economics) - Excellent communication skills, ideally with a proven track record of working directly with executive-level stakeholders to influence strategy. - Expertise in causal inference methods and forecasting. - Expertise in data querying languages (e.g. SQL) and scripting languages (e.g. Python, R). - Experience with data architecture technologies such as Airflow, Databricks, and dbt. Preferred qualifications: - Experience in technology, financial services and/or a high growth environment. - Experience with Excel and Finance systems (e.g. Oracle). 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

COMPANY OVERVIEW Lynx Analytics was founded in 2010 by a group of INSEAD students and professors with a strong research background in graph analytics. Several of our founders since then became professors and faculty directors of analytics centers at leading US universities. Our founding purpose? To apply graph theory to simplify and solve complex, real-world business problems. Our mission has evolved over the years, and we currently offer a range of cutting edge data analytics and AI solutions to help companies transform their operations and optimise their commercial performance. Back then, graph theory was mostly the purview of social networking sites. We wanted to expand this technology and help companies leverage their communities to unlock greater growth. Lynx has offices in Singapore, US, Hong Kong, Hungary, and operations in several other countries such as Canada, Germany, Indonesia. We work with some of the world’s largest companies and are constantly looking to expand our knowledge base and geographical footprint. Lynx Analytics’ technology is deployed with various Clients internationally and has significant growth potential. We have a diverse and inclusive global team comprising Professors, PhDs, MSc’s, and MBAs from Ivy Leagues, INSEAD and NUS with a broad spectrum of experience in start-ups and blue-chip companies (Google, Databricks, ZS, Abbvie, Amgen, Vodafone, Morgan Stanley, Palantir, Katana Graph to name but a few). It is the combination of our industry insight and experience, scalable proprietary technology, and highly qualified people that drives our compelling value proposition. We are looking for ambitious, innovative, empathetic and relentless team players to explore the career opportunities that we offer as we continue to scale our operations. About the role: We are looking for a Senior Data Scientist to work on and lead complex data analysis projects using standard modelling, data transformation approaches and Generative AI. They should be comfortable working with very large data sets residing in different data stores in disparate formats. The role requires the candidate to be strong with hands-on implementation and has the potential to move fast onto a high-growth career trajectory. Our future colleagues must have excellent communications skills as well as being able to visualize and present complex Data Science solutions for the main (C-level) stakeholders of the projects. Leadership experience and charisma are huge advantages. Key responsibilities will include: - Designing and Delivering Solutions for a defined Data Science Related Problem - Present the results / Prepare Presentations for the Project Stakeholders - Create reusable documentations, presentations and code libraries during the projects - Participating in internal education and research tasks - Leading smaller data science tasks with the help of internal leadership and PMO To succeed in this role, you should fulfill the following requirements: - More than 8 years of overall experience in data science field. - Experience in the life sciences industry is preferred - Mathematics, Statistics, Economics, Computer Science, Engineering or related degree (MSc or PhD is preferred) - Understanding complex Data Science Solutions - Solid knowledge of probability theory, statistics, data science algorithms and their application in Customer Retention, Campaign Management etc. areas - Good Communication (both verbal and written) and Data Visualization Skills - Coding abilities at least one of the following languages: Python (preferred), R, SAS, C, JAVA (or similar) - Solid experience in LLM / NLP Why You Will Love It Here: - Work on real-world AI and advanced analytics solutions with measurable business impact. - Collaborate with a global team of engineers and data scientists. - Exposure to diverse industries, modern cloud platforms, and cutting-edge AI technologies. - A collaborative culture that values real outcomes - High ownership, zero micromanagement - Rapid learning opportunities and diverse challenges - Flexible work hours, remote-friendly setup - Flat organisational hierarchy with high visibility and accessibility to our leaders

• Building and deploying predictive models • Working with large-scale datasets and turning them into actionable insights • Collaborating closely with data engineering and business teams • Covering the full analytics cycle: from hypothesis → modeling → deployment • Exploring modern areas like LLMs and AI-driven analytics.