Senior Data Scientist, Algorithm (Risk) - Airwallex

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 100,000 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 1,500 of the brightest and most innovative people in tech located across more than 20 offices across the globe. Valued at US$5.6 billion and backed by world-leading investors including Sequoia, Lone Pine, Greenoaks, DST Global, Salesforce Ventures and Mastercard, 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

As a global team, we span across Australia, China, USA, and Singapore, revolutionizing applied data science, data engineering and platform solutions to support Airwallex's rapid growth. You will collaborate with a diverse range of cross-functional partners, including Product, Engineering, Marketing, Sales, Finance, and more, to tackle complex data problems and shape the future of fintech

What you’ll do

Responsibilities:

• Optimize Airwallex’s overall risk portfolio effectively using experimentation and deep-dive analysis, and delivering insights to shape Airwallex’s overall risk strategy

• Apply machine learning and/or statistical models to estimate company-level risk exposure (e.g. acquiring, fraud, credit, FX, other product risks etc.) 

• Optimize our merchant-portfolio risk by leveraging a robust risk-adjusted margin model to inform onboarding decisions

• Objectively evaluate if our models and data are in compliance and aligned with the company’s risk strategy

• Partner with Risk Strategy, Risk Product, Engineering, Operations, and cross-functional teams to inform, influence, support, and execute Airwallex’s risk strategy

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:

• 5+ years industry experience and an advanced degree (PhD or MS) in a quantitative field (e.g. Statistics, Engineering, Sciences, Computer Science, Economics) 

• Experience with communicating the results of analyses to executives and cross-functional teams to influence the strategy 

• Expert in data querying languages (e.g. SQL), scripting languages (e.g. Python), and/or statistical/mathematical software (e.g. R), experience in schema design and dimensional data modeling a plus

• Expert in experimentation, machine learning, and statistical modeling to drive data-informed decisions, experience with deploying production machine learning models a plus 
  

Preferred qualifications:

• Experience in technology, financial services and/or a high growth environment is advantageous

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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.

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.