AI-Driven Data Scientist for Home Lending Growth

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

Job summary

CommBank is seeking a Data Scientist to build machine learning and agentic AI solutions that transform customer conversations in home lending. You will work with large datasets to uncover patterns, develop predictive models and deploy intelligent applications that turn data into practical insights.

You will own the data science lifecycle from framing problems to deployment and monitoring, balancing technical performance with responsible data use and business value.

Qualifications

• Strong foundations in statistics, mathematics and machine learning.
• Experience applying data science to complex, commercially meaningful problems.
• Ability to connect model and agent design decisions to outcomes, operations and business value.
• Experience with GenAI and agentic capabilities including workflow design and evaluation methods.

Responsibilities

• Identify and optimise customer cohorts for home lending opportunities using analytics and predictive modelling.
• Design, execute and measure experiments to evaluate propositions and generate actionable insights.
• Develop and apply AI-powered solutions, including GenAI and agentic capabilities to enhance customer communications and personalization.
• Determine whether problems are best addressed via ML, statistics, rules, or agentic applications, or a mix.
• Develop robust experimentation and evaluation frameworks for reliability, relevance and commercial impact.

Skills

Python

SQL

Statistics

Machine learning

GenAI / LLM

Experimentation

Communication

Tools

Python

SQL

Job description

CommBank is seeking a Data Scientist to build machine learning and agentic AI solutions that transform customer conversations in home lending. You will work with large datasets to uncover patterns, develop predictive models and deploy intelligent applications that turn data into practical insights.

You will own the data science lifecycle from framing problems to deployment and monitoring, balancing technical performance with responsible data use and business value.