Join a leading organisation in the resources sector as a Senior Data Scientist and play a key role in driving operational excellence through data-driven solutions. This is an opportunity to apply cutting-edge machine learning and advanced analytics to real-world challenges across planning, maintenance, and scheduling in a complex environment.

Key Responsibilities

• Analyse operational and sensor data from machinery to generate insights that support maintenance and planning improvements.

• Conduct root cause analysis and build predictive models to minimise unplanned equipment downtime.

• Develop and deploy machine learning models focused on predictive maintenance, scheduling, and asset optimisation.

• Write and maintain production-quality code that is scalable, efficient, and reliable.

• Design optimisation algorithms to improve task prioritisation and resource allocation.

• Collaborate with engineers, planners, and product managers to identify problems and deliver data-driven solutions.

• Work with data engineering and IT teams to integrate models and ensure smooth deployment in cloud environments.

• Support the design and delivery of digital products for planning and scheduling use cases.

• Lead discovery sessions to understand business challenges, define scope, and prioritise based on impact.

• Improve model accuracy and product performance using analytics and user feedback.

Essential Requirements of the ideal candidate

• 5+ years of experience in data science, machine learning, or advanced analytics with demonstrated experience with maintenance domain data in industrial or resources environments.

• Strong proficiency in Python or R and machine learning libraries.

• Experience working with SQL, big data platforms, and cloud environments.

• Exposure to Snowflake (certification highly regarded).

• Ability to write production-ready, maintainable code and deploy models at scale.

Thank you for your time and interest. Please note that we are only able proceed with those who have full working rights in Australia and are based in Brisbane for in office attendance requirements.