Senior Data Scientist — Financial Crime ML Validation

CommBank; Sydney; On-site; AUD 120,000 - 180,000; Full time

Job summary

CommBank is building a new capability within Risk Management to validate financial crime models as the bank moves from rule-based alerts to modern ML approaches.

You will apply data science and ML expertise to assess models that identify potentially risky transactions, deep-dive into prior projects, and collaborate with stakeholders in a regulated environment to strengthen model risk outcomes.

Qualifications

• Bring hands-on experience in Data Science, Machine Learning and Statistical Modelling.
• Clearly articulate how you have solved complex problems and designed technical approaches in previous model-related work.
• Strong Python capability, AWS experience, and experience in a data-science-led environment.
• Financial crime domain knowledge or experience with financial crime models, either in development or validation.
• Ability to work with stakeholders and apply sound judgement in regulated, risk-focused environments.
• Hold a tertiary qualification in Statistics, Mathematics, Data Science or Computer Science.

Responsibilities

• Validate financial crime models as part of a dedicated team validating a new capability.
• Apply data science and ML expertise to assess models detecting patterns and risky transactions.
• Deep dive into past projects and technical decisions to inform validation of complex use cases.
• Collaborate with stakeholders in a heavily regulated domain to ensure governance and technical quality.

Skills

Data Science

Machine Learning

Python

AWS

Statistical Modelling

Education

Statistics, Mathematics, Data Science or Computer Science degree

Job description

CommBank is building a new capability within Risk Management to validate financial crime models as the bank moves from rule-based alerts to modern ML approaches.

You will apply data science and ML expertise to assess models that identify potentially risky transactions, deep-dive into prior projects, and collaborate with stakeholders in a regulated environment to strengthen model risk outcomes.