Level

Commonwealth Bank of Australia

Principal Data Scientist

Commonwealth Bank of Australia is hiring a Principal Data Scientist. Level rates it ; you can apply on Commonwealth Bank of Australia.

Level's read on this job

  • Builds AI ●●●● is how Level rates this data scientist role: the job is building AI systems.
  • Tools and skills the posting cites include LangChain, LangGraph and PyTorch.
  • Level classifies it as a lead role.
  • Level lists 190 other open roles at Commonwealth Bank of Australia.
  • The role was posted in the last few days.

AI in this role

langchainlanggraphpytorchtensorflow
prompt-engineeringragai-evaluationnlp

Business & Team: Financial Crime AI Team, COO

 

See yourself in our team:

At the heart of the Chief Operations Office, we’re driving CBA’s ambition to detect, deter and ultimately eliminate financial crime through the power of transformative AI solutions. 

As a Principal Data Scientist in Financial Crime domain, you’ll be a hands-on technical leader working in cross-functional squads with engineers and other experts to design and deliver innovative, responsible and high impact AI solutions. You’ll stay at the forefront of emerging technologies, fostering a culture of curiosity, inclusion, and continuous learning in the team, and translating complex ideas into high-impact outcomes for our customers and colleagues. You’ll contribute strongly to guide the team on technically demanding problems across Fin Crime domain as AI continues to evolve.

 

If you’re passionate about shaping the future of AI while helping others thrive, we’d love you to lead with us.

 

Key Responsibilities:

  • Lead GenAI and Agentic system design and development: Prototype, design, develop, deploy and design monitoring for innovative GenAI and Agentic solutions that transform the prevention, detection and investigation of Financial Crime in the bank while setting technical direction and best practices across the domain.
  • Develop and implement Machine Learning Models: Prototype, design, build, deploy and monitor a variety of machine learning models 
  • Expand Data/AI Science Usage: Identify potential AI opportunities across Fin Crime for future focus
  • Drive Continuous Improvement and Business Outcomes: Work closely with financial crime stakeholders to understand risk and operational pain points, driving adoption of AI and GenAI to enhance detection, improve investigative outcomes, and shape the future operating model

 

We’re interested in hearing from people with the following skills.

 

Technical Leadership and Coordination:

  • Ability to coordinate effectively across the Fin Crime and COO Data Science community to unify efforts and drive collaborative outcomes.
  • Demonstrated capability in driving outcomes that are reusable and can be leveraged across the group.
  • Intellectual curiosity and ability and willingness to learn, experiment and lead new to the world concepts 

 

Technical Skills and Tools:

  • Generative AI: Experience designing and deploying various aspects of Generative AI, including prompt engineering, Retrieval-Augmented Generation (RAG), guardrail design, orchestration design, skills, evals frameworks, and tools such as LangChain and LangGraph.
  • Machine Learning: Expertise in the design and construction of machine learning models, including natural language processing, unsupervised clustering and classification algorithms including feature engineering, model selection, hyperparameter tuning, model evaluation, and deployment of models into production environments.
  • Cutting-Edge AI: Work on projects leveraging traditional AI, Generative AI, and agentic AI to pave the path for how banking and broader industry functions in the future.
  • Version Control and CI/CD: Experience with version control and CI/CD pipelines such as GitHub.
  • Tooling: Hands-on experience with data science tools such as Spark, Python, R, TensorFlow, PyTorch, and SQL.
  • Data Engineering: Knowledge of ETL processes and data pipeline construction, ensuring efficient data flow and integration.
  • Deployment: Familiar with Docker for containerization and deployment of applications.
  • Cloud Architecture: Grounded understanding in AWS or Azure Cloud architecture and integration, demonstrating experience in deploying and managing cloud-based solutions.

Working with us:  

Our people bring their diverse backgrounds and unique perspectives to build a respectful, inclusive, and flexible workplace with an ultimate flexibility to work from any of our engineering hubs within Australia. One where we’re driven by our values, and supported to share ideas, initiatives, and energy. One where making a positive impact for customers, communities and each other is part of our every day.

 

If you're already part of the Commonwealth Bank Group (including Bankwest, x15ventures), you'll need to apply through Sidekick to submit a valid application. We’re keen to support you with the next step in your career.

We're aware of some accessibility issues on this site, particularly for screen reader users. We want to make finding your dream job as easy as possible, so if you require additional support please contact HR Direct on 1800 989 696.

Advertising End Date: 29/10/2026

How we rate this

Principal Data Scientist at Commonwealth Bank of Australia rates 92 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Prompt engineeringRAGAI EvaluationNLPLangChainLangGraphPyTorchTensorFlow

Also common in data scientist jobs at this level

Pay

Typical data scientist job
$166k

This posting doesn't list pay. Use the typical data scientist figure when pay comes up. See data scientist jobs

Questions you could be asked

  1. How do you structure and test a prompt to get consistent output from a language model?
  2. How would you design a retrieval step so the model answers from real data instead of guessing?
  3. How do you decide that one model's output is better than another's for a given task?
  4. What NLP problem have you worked on, and how did you measure whether it actually worked?
  5. Walk me through how you've used LangChain in your day-to-day work.

Adapt your resume

  • List these exact terms on your resume: Prompt engineering, RAG, AI Evaluation, NLP, and LangChain. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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