Staff Data Scientist, Risk

Job description

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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 1,700 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.
About The Team
The Product team at Airwallex is a group of passionate builders and problem-solvers who are obsessed with crafting customer-centric solutions that empower businesses to operate anywhere, anytime.

We combine technical expertise with a deep understanding of our customers' needs to design and build unified, intuitive, and scalable products.

As a team, we thrive in a collaborative and fast-paced environment, constantly iterating and pushing boundaries to deliver exceptional product experiences.
What You’ll Do
Airwallex is building a centralised risk platform that will harness advanced analytics and machine learning techniques to enable real-time decisioning across a diverse range of product lines and platforms.

The risk product domain is responsible for building the platforms, engines & case management whilst also delivering the risk models so you will be involved in crafting the full end-to-end risk capability.
This role will work on building out Airwallex’s risk product domain focusing on analyzing the compliance impact on transactions as well as delivering a financial crime detection and prevention solution.

You will be responsible for conducting risk analysis on transaction level and merchant level, developing risk detection framework, and empowering business.
This role is based in Singapore.
Responsibilities

• Design, develop and deploy credit risk models for SME lending, using internal transaction and customer data as well as third-party data sources.
  
  
• Own the end-to-end modeling lifecycle: feature engineering, algorithm selection, validation, calibration and performance monitoring.
  
  
• Source, ingest and validate external datasets (e.g. credit bureaus, alternative data) to enrich underwriting inputs.
  
  
• Implement automated model monitoring, alerting on data drift, performance degradation and key metric breaches.
  
  
• Collaborate with underwriting, product and engineering teams to translate business requirements into modeling solutions.
  
  
• Mentor junior analysts (future hires) and, as the team grows, guide a small modeling squad to deliver end-to-end credit analytics.
  

• Bachelor’s degree in Mathematics, Statistics, Finance, Economics, Operations Research, or a related quantitative discipline.
  
  
• 5+ years of hands-on experience in credit or risk analytics or modelling, preferably in SME lending or commercial banking.
  
  
• Demonstrated expertise sourcing, ingesting and validating external data (e.g., credit bureaus, alternative data providers).
  
  
• Proficient in SQL and at least one scripting language (Python, R, or Shell) for data extraction, feature engineering and model development.
  
  
• Strong business acumen in underwriting processes and risk management, with the ability to translate commercial requirements into analytic solutions.
  
  
• Excellent stakeholder management skills, with a track record of working cross-functionally to deliver data-driven insights.
  

• 7+ years of hands-on experience in credit or risk analytics or modelling, preferably in SME lending or commercial banking.
  
  
• 1+ year of experience leading or mentoring a small analytics team through the end-to-end modeling lifecycle.
  
  
• Demonstrated ability to balance technical rigor with business priorities in a fast-paced, early-stage environment.
  
  
• Strong communicator who can distill complex model assumptions and performance metrics for non-technical audiences.
  
  
• Familiarity with BI/visualization tools (Looker, Tableau or equivalent) to build dashboards that inform underwriting decisions.
  
  
• Working knowledge of MLOps or workflow orchestration frameworks (e.g., Airflow, Spark, Git) to ensure scalable and robust model pipelines.
  

Seniority level

• Seniority level

  Mid-Senior level

Employment type

• Employment type

  Full-time

Job function

• Job function

  Engineering and Information Technology

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