Senior Data Scientist, Algorithm

AIRWALLEX (SINGAPORE) PTE. LTD.; Singapore; On-site; SGD 80,000 - 100,000; Full time

Job summary

A global financial technology company in Singapore is seeking a data scientist with 5+ years of experience and an advanced degree in a quantitative field. The role involves designing experiments to enhance payment success rates and collaborating with cross-functional teams. The ideal candidate is proficient in SQL, Python, and statistical modeling, with experience in machine learning being a plus. This position offers the opportunity to make significant contributions in a fast-paced environment.

Qualifications

• 5+ years of industry experience in a quantitative field.
• Ability to communicate analytical results to executives.
• Expertise in data querying and scripting languages.
• Experienced in experimentation and statistical modeling.

Responsibilities

• Design and implement experiments to improve payment success rates.
• Develop optimization algorithms to enhance payment success.
• Build predictive models to estimate payment success probabilities.
• Collaborate with data engineers on optimization opportunities.
• Partner with cross-functional teams to support product strategies.

Skills

Data querying languages (e.g. SQL)

Scripting languages (e.g. Python)

Statistical modeling

Machine learning

Communication skills

Education

PhD or MS in a quantitative field

Job description

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.

Responsibilities:

• Design and implement rigorous experiments to improve payment success rates
• Develop optimization algorithms (e.g. contextual multi‑armed bandits) to improve payment success rates with minimal opportunity cost
• Build transaction‑level predictive models that accurately estimate probability of payment success
• Partner with data engineers to develop tools to identify optimization opportunities at scale
• Partner with Product, Engineering, Risk, and cross‑functional teams to inform, influence, support, and execute product strategy and investment decisions

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