About Us
At ANZ, we're harnessing technology and data to improve the financial wellbeing and sustainability of our millions of customers.
About the Role
As a Machine Learning Engineer/Data Scientist at ANZ, you'll build advanced models to solve complex data problems through advanced modelling. You'll work with Data Engineers and Data Analysts to determine relevant internal and external data sources for developing predictive and descriptive models. You'll apply analytical skills to a broad range of data points to develop customer-centric solutions and communicate insights and models through impactful data visualisation and storytelling.
Your Day
You'll design, train, and implement machine learning models using Python and libraries like scikit-learn, tensorflow, pytorch to optimize business processes and automate decision-making. You'll deploy and scale machine learning solutions leveraging MLOps practices and CICD pipelines. You'll monitor model performance and implement corrective action to address any degradation or issues. You'll build and automate data pipelines using Airflow, Docker, and Kubernetes pods. You'll address and solve complex business issues using large amounts of data. You'll develop tools and methods to scientifically profile customers and customer segments, products, and channels, and associated costs, revenues, risks, and opportunities. You'll source data from various sources to combine, synthesise, and analyse to support campaigns, pricing, propositions, and other decisions. You'll initiate, design, and implement innovative capabilities in the field of data science. You'll lead, optimise, design, and execute business interventions (customers and operational) to uplift customer engagement and business performance.
What We Need
We need someone proficient in programming with Python (including data science libraries such as scikit-learn, Tensorflow, and PyTorch). They should have expertise in data query languages such as SQL (Trino, Teradata, and ANSI SQL flavours). They should also have strong expertise in predictive modelling, pattern recognition, clustering, supervised, and unsupervised learning techniques. Experience in building and deploying end-to-end pipelines for training, deployment, and monitoring using Airflow, with integrated data quality checks to ensure reliability and performance is required. Experience with containerization using Docker, Kubernetes for orchestration and scaling ML models, and MLFlow for model tracking and versioning is necessary. Experience using Evidently or similar packages to monitor model performance in production is desirable. A good understanding of generative AI, NLP domain, and RAG architecture is essential. Exposure to langchain and huggingface is an advantage. Strong ability to translate data insights into practical business recommendations is required.
Why Join Us
At ANZ, you'll be doing meaningful work that will shape a world where people and communities thrive. You'll feel it too because you'll have the resources, opportunities, and support you need to take the next big step in your career. We're a diverse bunch at ANZ, and we celebrate the different backgrounds, perspectives, and life experiences of our people. We welcome applications from everyone and encourage you to talk to us about any adjustments you may require to our recruitment process or the role itself.