At a Glance
- Tasks: Design and implement experiments to enhance payment product experiences and success rates.
- Company: Join Airwallex, a leading AI-native financial operating system powering global commerce.
- Benefits: Competitive salary, diverse team, and opportunities for professional growth.
- Other info: Mentor junior scientists and influence product strategy in a dynamic environment.
- Why this job: Make a real impact in the fintech space with innovative data-driven strategies.
- Qualifications: 7+ years in data science with strong skills in SQL and Python/R.
The predicted salary is between 63000 - 77000 £ per year.
About Airwallex
Airwallex is the AI-native financial operating system for a real-time, intelligent economy. More than 675,000 businesses, including McLaren Racing, Qantas, SHEIN, and TikTok, use us, directly or through our platform partners, to run their financial operations or build and monetize financial products of their own. We started in Melbourne in 2015 to build the infrastructure global commerce runs on. We're the regulated backbone behind global payments: not by accident, but by design. A decade plus, 85+ licenses, and a financial infrastructure spanning North America, Europe, the Middle East, and Asia-Pacific. We're co-headquartered in San Francisco and Singapore, with more than 2,700 people across 27 offices. We hire builders with founder-level energy, people who move fast with good judgment, dig in with real curiosity, and make calls from first principles rather than waiting to be told what to do.
What You’ll Do
- Design and implement rigorous frontend experiments to improve the payment product experience and payment success rates.
- Identify and quantify opportunities to improve and grow the payments product, and drive roadmaps with those insights.
- Translate open-ended business problems into clearly defined data science initiatives.
- Build scalable measurement frameworks and strategic north star metrics to enable consistent, data-driven decision-making across teams that directly informs product strategy.
- Communicate data insights and recommendations to technical and non-technical audiences, ensuring findings influence stakeholders.
- Mentor and coach junior data scientists, and act as the single-threaded technical owner on cross-functional initiatives.
Qualification
- 7+ years industry experience and a degree in a quantitative field (e.g. Statistics, Engineering, Sciences, Computer Science, Economics). An advanced degree (MS or PhD) is a bonus.
- Proven experience in experimentation and causal inference to drive data-informed product decisions with concrete impact.
- Excellent communication and interpersonal skills; proven ability to influence product strategy in high-growth environments.
- Strong skills in SQL and Python/R; experience with data modeling a plus.
- Experience in technology, financial services and/or a high growth environment is advantageous.
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.
Senior Data Scientist, Analytics (Risk & Payments) • London EMEA , London London employer: Airwallex
Airwallex is an exceptional employer that fosters a dynamic and innovative work culture, empowering employees to make a real impact in the global financial landscape. With a commitment to professional growth, team members benefit from accelerated learning opportunities and the chance to collaborate with some of the brightest minds in tech across a diverse, global environment. Located in Melbourne, Airwallex offers a unique blend of cutting-edge technology and a supportive atmosphere, making it an ideal place for those seeking meaningful and rewarding careers.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Data Scientist, Analytics (Risk & Payments) • London EMEA , London London
✨Get Involved in Data Science Meetups
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Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Airwallex.
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We think you need these skills to ace Senior Data Scientist, Analytics (Risk & Payments) • London EMEA , London London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Airwallex, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Airwallex. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Airwallex
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Airwallex!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
