Senior Data Scientist - Machine Learning & Generative AI

About the role

A growing data and AI business, expanding in Melbourne since early 2024, is building its local team. It works with utility providers, infrastructure, telecommunications and retail clients, using machine learning, advanced analytics and modern data platforms. The client is helping a major electricity distributor build a smart meter data platform, develop supervised and unsupervised models and integrate generative AI tools so business users can access data and model outputs through their workflows.

Key responsibilities

• Define the problem with client stakeholders, explore complex datasets, engineer features and build supervised and unsupervised models
• Evaluate model performance, take ownership of modelling from investigation to production and work with data engineers on training and prediction pipelines
• Write maintainable, tested code and manage deployments using Git, CI/CD and Declarative Automation Bundles
• Monitor model performance, explain results and limitations, and apply generative AI to improve access to data and models
• Help colleagues build data science skills through joint development, code reviews and training, and set consistent modelling and engineering practices

Required profile

• Degree in a quantitative discipline (engineering, computer science, mathematics, statistics or physics) or equivalent experience
• Strong Python skills with pandas, scikit-learn, NumPy and SciPy
• Substantial experience with supervised and unsupervised machine learning and building complete ML pipelines
• Experience deploying models into production and using MLflow or similar tools
• Experience with Databricks and cloud services such as AWS or Azure; PySpark for large datasets
• Git, code review, automated testing and CI/CD
• Existing Australian work rights strongly preferred due to the required start date

What we offer

• Competitive salary and performance-based bonus scheme
• Annual leave, public holidays and employer-paid superannuation
• Learning and development budget, laptop and equipment provided
• Hybrid working and access to Databricks and cloud infrastructure