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Commonwealth Bank of Australia

Data Scientist

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

Level's read on this job

  • Level rates this data scientist role Builds AI ●●●●: the job is building AI systems.
  • Tools and skills the posting cites include OpenAI, Claude and LangChain.
  • Level lists 201 other open roles at Commonwealth Bank of Australia.
  • The role was posted in the last few days.

AI in this role

openaiclaudelangchainlanggraphxgboostclaude-codecodex
ragfine-tuningai-safety

Data Scientist | AI & Model Validation

  • Independently test and challenge some of the Group’s most important machine learning, Generative AI and agentic AI use cases.
  • Build hands-on expertise across emerging AI architectures, evaluation methods and responsible AI.
  • Create reusable evaluations, test harnesses and automation to help strengthen how AI is validated at scale.

Do Work That Matters

AI & General Model Validation (AIGMV) provides independent technical challenge and review of important machine learning and AI models across the Group.

As a Data Scientist in the team, you’ll help assess whether AI solutions perform as intended by independently evaluating their data, methodology, assumptions and outcomes. You’ll combine hands-on technical investigation with clear, evidence-based challenge, helping support safe and sustainable AI adoption.


See yourself in our team

You’ll work closely with model developers, product owners and business stakeholders while maintaining the independence expected of a validation function.

In this role, you will:

  • Independently deliver defined components of AI and ML validation, including technical analysis, testing and evaluation.
  • Review data, methodologies, assumptions and model outcomes, investigating unexpected behaviour and potential weaknesses.
  • Design and execute evaluations for Generative AI and agentic systems, including architectures involving RAG, tool use, guardrails, routing, memory and LLM-as-a-Judge approaches.
  • Build reproducible test harnesses, reusable evaluations and automation that improve the efficiency and consistency of validation.
  • Navigate unfamiliar codebases, reproduce evaluation pipelines and inspect trace data to understand model and agent behaviour.
  • Provide constructive, evidence-based technical challenge and bring your findings into broader validation conclusions.
  • Communicate complex technical findings clearly to both technical and non-technical stakeholders.
  • Share knowledge and contribute to the continuing development of AI validation practices, tooling and standards.

We’re interested in hearing from people who:

This role will suit a curious, hands-on Data Scientist who enjoys getting beneath the surface of how AI systems behave. You’ll bring sound analytical judgement, attention to detail and the confidence to investigate unexpected results, while staying curious as Generative AI and agentic techniques continue to evolve.


Experience across several of the following will help you succeed:

  • Advanced Python skills and experience with GitHub, version control and CI/CD; SQL, R or TypeScript exposure is also valuable.
  • Experience with frameworks and tools such as LangChain, LangGraph, OpenAI Agents SDK, Pydantic and evaluation or observability platforms such as Langfuse or HoneyHive.
  • Practical knowledge of Generative AI and agentic architectures, including orchestrators, sub-agents, RAG pipelines, tool use, guardrails, multi-agent routing and memory.
  • Hands-on experience building or evaluating AI and Generative AI solutions, including synthetic data; transformer fine-tuning experience would be beneficial.
  • Experience using AI coding agents such as Claude Code or Codex, including building reusable skills, agents, hooks or plugins to automate technical workflows.
  • Working knowledge of traditional machine-learning approaches such as regression, random forests and XGBoost, together with appropriate evaluation methods.
  • Confidence reading unfamiliar code, reproducing evaluation pipelines, writing test harnesses and analysing trace data.
  • Strong communication skills and the ability to explain complex analysis and technical risks clearly to different audiences.

If AI safety matters to you, you enjoy learning independently and you bring strength across some rather than all of these areas, we’d like to hear from you.

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: 10/11/2026

How we rate this

Data Scientist at Commonwealth Bank of Australia rates 95 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

RAGFine-tuningAI SafetyOpenAIClaudeLangChainLangGraphXGBoost

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 would you design a retrieval step so the model answers from real data instead of guessing?
  2. Walk me through fine-tuning a model: what data did you use, and how did you check the result?
  3. How do you think about the risk of an AI system in this kind of role failing silently?
  4. What's a project where you used OpenAI hands-on?
  5. Walk me through how you've used Claude in your day-to-day work.

Adapt your resume

  • List these exact terms on your resume: RAG, Fine-tuning, AI Safety, OpenAI, and Claude. 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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