Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Staff Data Scientist, EMEA

Airwallex Pty Ltd.
City of London
2 days ago
Create job alert
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 150,000 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 1,800 of the brightest and most innovative people in tech across 26 offices around the globe. Valued at US$6.2 billion and backed by world‑leading investors including Visa, Airtree, Blackbird, Sequoia, DST Global, Greenoaks, Salesforce Ventures, Lone Pine, and Square Peg, 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 the Head of EMEA
  • Partner with Sales, Marketing, Finance, Data Engineering, and Product to align metric definitions/dimensions and data sources across the business
  • 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 building tools that aggregate and evaluate forecasts and 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.


#J-18808-Ljbffr

Related Jobs

View all jobs

Data Scientist - Citi

Data Scientist (Football Club)

Data Scientist (Football Club)

Staff Data Scientist

Staff Data Scientist

Staff Data Scientist

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Data Science Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.