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Senior Data Scientist, Analytics (Regulatory Reporting)

Airwallex· Product
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SG - SingaporeFull-timePosted 7mo ago

About the role

Senior Data Scientist focused on analytics for regulatory reporting and risk/compliance. Owns analytical layer of regulatory data infrastructure, defines what 'correct' means, builds data foundations, validation frameworks, and self-service analytics for regulators, auditors, and leaders. Based in Singapore with relocation options.

Responsibilities

  • Lead analytics and data design for priority regulatory reports (e.g. regulatory compliance, financial crime, and financial regulatory returns), from requirement translation through to data logic, testing, and go‑live.
  • Partner with Regulatory Compliance, FCC, Finance, DataOps and Data Engineering to standardise how we use common dimensions across reports, leveraging the regulatory reporting data foundation.
  • Co-design data models and mart-layer logic with Data Engineering — not just a consumer of tables, but an active voice in how reporting dimensions are structured, tested, and versioned upstream.
  • Work with Data Engineering to define automated test suites and manual QA playbooks that must pass before reports are submitted to regulators.
  • Conduct deep‑dive analyses when anomalies, RFIs, or audit findings arise – quantifying impact, identifying root causes across products/entities, and proposing remediation options.
  • Shape requirements for a scalable regulatory reporting and risk analytics platform
  • Champion best practices in analytical rigour, documentation, and validation for colleagues working on risk and compliance topics; provide informal mentorship to analysts and data scientists in adjacent teams.

Qualifications

  • Bachelor's degree in Mathematics, Statistics, Operations Research, Finance, Economics or 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 proven risk management experience or equivalent, including analytics, modeling implementation, and stakeholder management
  • Familiarity with analytics engineering practices: writing modular, tested, documented SQL transformations (e.g. dbt or equivalent), and thinking in layers: source → staging → marts
  • Experience building exploratory and production-grade analytics using modern data tools (e.g. Hex, Evidence, or notebook-style environments where SQL, Python, and visualisation coexist in the same workflow)
  • Excellent communication and stakeholder-management skills: able to explain technical data topics to non-technical audiences, document assumptions clearly, and influence cross-functional partners across Legal, Risk, Compliance, Finance, and Engineering

Skills

Required:SQLPythondbt / analytics engineeringCommunication / stakeholder management
Nice to have:Regulatory reporting
Last updated Oct 7, 2026. Interested? Apply on the company site.
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