Senior Data Scientist, Anaytics (Risk & Compliance)

SG - Singapore · 5+ years experience · Full-time · Onsite · Posted Oct 5, 2026

At a glance

Airwallex is the AI-native financial operating system for a real-time, intelligent economy. More than 676,000 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 2,300 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 https://www.airwallex.com/en-us/operating-principles to see it in full.

The Senior Data Scientist, Analytics (Risk & Compliance) will own the analytical layer of Airwallex's regulatory data infrastructure.
This role involves defining regulatory data logic, building validation systems, and partnering with compliance, engineering, and product teams to ensure safe and sustainable global growth.

Lead analytics and data design for priority regulatory reports, including financial crime and regulatory compliance returns.
Partner with Regulatory Compliance, FCC, Finance, DataOps, and Data Engineering to standardize common data dimensions.
Co-design data models and mart-layer logic with Data Engineering teams.
Define automated test suites and manual QA playbooks for regulatory report submissions.
Conduct deep-dive analyses to identify root causes and quantify impact when anomalies or audit findings arise.
Shape requirements for a scalable regulatory reporting and risk analytics platform.
Champion best practices in analytical rigor, documentation, and validation across the organization.
Provide informal mentorship to analysts and data scientists in adjacent teams.

Bachelor's degree in Mathematics, Statistics, Operations Research, Finance, Economics, or a related quantitative discipline.
5+ years of hands-on experience extracting and manipulating large data with SQL.
Experience with scripting in Python, Shell, or R.
5+ years of proven risk management experience, including analytics, modeling implementation, and stakeholder management.
Familiarity with analytics engineering practices, including writing modular, tested, and documented SQL transformations.
Experience building exploratory and production-grade analytics using modern data tools like Hex or Evidence.
Excellent communication and stakeholder-management skills for cross-functional collaboration.

Direct experience working on regulatory reporting for the risk and compliance domain.
Hands-on experience with dbt (models, tests, docs, macros) and a modern data warehouse such as BigQuery, Databricks, or Snowflake.
Understanding of data pipeline design and the ability to discuss architectural trade-offs with Data Engineers.
Exposure to model risk or AI/ML governance, including model inventories, validation, and monitoring.
Experience working with cross-functional, geographically distributed teams.