Staff Data Scientist, Algorithm, Financial Markets
Airwallex is hiring a Staff Data Scientist, Algorithm, Financial Markets in Singapore, Singapore. Level rates it ; you can apply on Airwallex.
Level's read on this job
- On Level's scale, this data scientist role is Little AI โโโโ: aI is not part of the work.
- Level classifies it as a lead role.
- Level lists 498 other open roles at Airwallex.
AI in this role
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. Read our operating principles to see it in full.
ยHow you'll make impact
The Financial Markets team is building the data science capabilities that support FX pricing, execution and risk decisions. You'll own the modelling and analysis roadmap for the FX product and turn rigorous statistical and machine learning work into better trading, product and commercial decisions.
Own and drive the technical direction and modelling roadmap for the FX product, identifying high-impact opportunities and prioritising work in collaboration with Product Managers.
Develop and productionise models to forecast and monitor FX rate volatility, providing actionable insights to support trading and execution strategy.
Apply statistical and machine learning models to optimise FX trade execution, focusing on minimising cost, improving routing efficiency and managing market risk exposure.
Establish robust backtesting, validation, monitoring and governance practices for financial-market models.
Partner with Product Managers, Traders, Risk, Engineering and other stakeholders to translate analytical findings into trading and product decisions.
Define success metrics and evaluate the incremental impact of models and strategies in production.
Move quickly from analysis to decision-ready recommendations, communicating clearly with executives and cross-functional partnersโeven when the data or problem is ambiguous.
Conduct customer and commercial analysis where it directly informs FX strategy, pricing or product priorities.
You'll work from our Singapore office five days a week.
What we're looking for
Essentials
7+ years of industry experience and an advanced degree, such as a PhD or MS, in a quantitative field such as Statistics, Engineering, Sciences, Computer Science or Economics.
Significant experience applying statistics, machine learning or optimisation to financial markets, trading, fintech or other dynamic decision systems.
Strong understanding of FX, execution, pricing, liquidity, market risk or related quantitative domains.
Expert Python skills and strong SQL skills; experience with R or other quantitative tools is a plus.
Strong foundations in time-series modelling, statistical inference, optimisation and experimental design.
Ability to do schema design and dimensional data modelling, taking models from research/prototyping into reliable production systems.
Proven ability to set direction, influence and communicate with senior stakeholders and deliver through cross-functional teams.
Preferred
Experience in technology, financial services, a trading environment and/or a high-growth environment.
You'll thrive here if
You're comfortable owning the roadmap yourself.
You're comfortable making decisions with incomplete information and moving quickly anyway.
You connect dots across functions and hold the big picture without losing grip on the details.
You simplify complexity rather than hiding behind it.
You can advocate hard for a position, then fully support the team's direction once a call is made.
Learn more about your team
Data Science at Airwallex is evolving from individual analyses and standalone models into scalable systems of decision and action. We bring together two complementary capabilities: DS Analytics, which improves decision quality and reduces decision latency, and DS Algorithm, which directly optimizes product and business outcomes in real time.
Youโll build systems of decision that help teams understand what changed, determine what to do next, and learn whether the action worked. This means owning the AI analytics harness around the analysisโincluding semantic layers, context retrieval, evaluation infrastructure, validation gates, and governanceโso decisions remain reliable, contextualized, and trustworthy at scale.
Youโll also build systems of action that use machine learning and AI to shape product behavior, ranking, pricing, risk, allocation, and customer experiences. The work extends beyond training models to designing how intelligent systems reason, use tools, operate within guardrails, execute workflows, and measure their incremental impact.
As AI makes ad hoc analysis increasingly accessible, the remaining advantage is scale, scientific method, and rigor. Youโll structure ambiguous problems, design experiments, identify confounding factors, challenge misleading conclusions, and build production systems that produce reliable outcomes repeatedlyโnot just one-off answers.
The work is moving up the stack: from analysis to system, from model to harness, and from answering a question once to building something that answers it correctly over time. If you want to create data science systems that compound decision quality and turn intelligence into measurable action, this is where that work happens.
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 donโt 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.
How we rate this
Staff Data Scientist, Algorithm, Financial Markets at Airwallex rates 11 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.
Little AI. AI is not part of the work.
- โโโโ Builds AI80 to 100
- โโโโ Works on AI60 to 79
- โโโโ Uses AI40 to 59
- โโโโ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Pay
- Typical data scientist job
- $165k
This posting doesn't list pay. Use the typical data scientist figure when pay comes up. See data scientist jobs
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