Please note, candidates must be able to accommodate a start date of 18th May.
Join one of Australia’s Big 4 Banks as an AI data Scientist in a new enterprise AI function of the bank. This is a rare opportunity to work at the forefront of applied AI while helping shape the next generation of banking technology.
These are some of the most sophisticated AI systems being built in financial services today. They demand cutting‑edge thinking in agent architecture, evaluation methodology, and safety engineering, where reliability is critical and mistakes have real‑world financial impact.
You’ll work closely with the Head of Data Science and collaborate within small, highly technical squads where experimentation, rapid decision‑making, and intellectual honesty are valued more than hierarchy or bureaucracy.
Key Responsibilities
* Design and build agentic AI systems, including reasoning loops, orchestration logic, tool integrations, memory frameworks, and context management.
* Translate AI architecture into production‑ready solutions, bridging the gap between data science experimentation and scalable engineering implementation.
* Develop prompting strategies and retrieval pipelines, while running rapid experimentation to improve agent performance and reliability.
* Implement evaluation, monitoring, and safety frameworks to detect issues such as hallucinations, drift, or unexpected agent behaviour and ensure systems operate within defined guardrails.
* Strengthen the broader AI capability by building reusable tools, sharing knowledge across teams, and mentoring junior data scientists.
Skills & Experience
* Degree in a quantitative discipline such as computer science, machine learning, statistics, mathematics, physics, or engineering.
* Proven experience delivering AI or machine learning systems into production environments.
* Hands‑on experience across machine learning and applied AI, with strong depth in areas such as NLP, recommender systems, ML modelling, or LLM‑based applications.
* Strong Python development skills and experience working with modern ML and AI ecosystems.
* Practical experience across the ML lifecycle: data exploration, feature engineering, model development, evaluation, experimentation, and deployment.
* Exposure to foundation model APIs, prompt engineering, embeddings, retrieval techniques, or contextual data pipelines.
* Strong grounding in statistics, probability, and experimental design to rigorously evaluate AI models and systems.
To be eligible for this position, candidates must have full working rights in Australia.
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