Senior Data Scientist, Analytics (Risk & Compliance)

About the role

Airwallex, the AI-native financial operating system used by hundreds of thousands of businesses worldwide, is hiring a Senior Data Scientist, Analytics (Risk & Compliance) in its Product team. The role owns the analytical layer of the regulatory data infrastructure, partnering with Regulatory Compliance and Financial Crime Compliance teams to build data foundations, validation frameworks and self-service analytics for regulatory reporting and risk oversight. It is based in Singapore, with relocation possible.

Key responsibilities

• Lead analytics and data design for priority regulatory reports (compliance, financial crime, regulatory returns), from requirement translation through data logic, testing and go-live.
• Partner with Regulatory Compliance, FCC, Finance, DataOps and Data Engineering to standardise common dimensions (accounts, FX, products, entities) across reports.
• Co-design data models and mart-layer logic with Data Engineering, and help define automated test suites and manual QA playbooks for regulator submissions.
• Run deep-dive analyses on anomalies, RFIs or audit findings, quantify impact, identify root causes and propose remediation.
• Shape requirements for a scalable regulatory reporting and risk analytics platform.
• Champion analytical rigour, documentation and validation, and mentor analysts and data scientists in adjacent teams.

Required profile

• 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, and scripting in Python, Shell or R.
• 5+ years of proven risk management experience or equivalent, including analytics, modelling implementation and stakeholder management.
• Ability to explain technical data topics to non-technical audiences and influence cross-functional partners across Legal, Risk, Compliance, Finance and Engineering.

Required skills

• Familiarity with analytics engineering: modular, tested, documented SQL transformations (e.g. dbt or equivalent) and layered thinking (source, staging, marts).
• Exploratory and production analytics with modern data tools such as Hex, Evidence or notebook-style environments.
• Preferred: direct regulatory reporting experience, hands-on dbt and a modern data warehouse (BigQuery, Databricks or Snowflake), exposure to model risk or AI/ML governance, and experience with distributed teams.