Description

Data Scienctist

Perth

Permanent $160-190k

The Opportunity

An established organisation is accelerating its adoption of Artificial Intelligence to enhance operational performance across complex, large-scale assets. This role sits at the forefront of that transformation, focused on designing and deploying AI-driven solutions that improve master data quality, asset reliability, and work management processes.

The objective is clear: deliver measurable improvements in cost efficiency, uptime, and workforce productivity across integrated operations. This is a hands-on, high-impact role embedded within operational environments, partnering directly with end users to rapidly develop and deploy practical AI products.

This position is best suited to a senior individual contributor who thrives in autonomous, fast-moving environments and enjoys solving real operational problems using advanced analytics and AI techniques.

Key Responsibilities

• Lead the end-to-end design, development, deployment, and ongoing support of AI and analytics solutions.
• Identify and quantify operational improvement opportunities using structured and unstructured data sources.
• Partner with reliability engineers, asset managers, and operational teams to define technical requirements and translate business challenges into analytical solutions.
• Develop, test, validate, and refine models in collaboration with frontline teams to ensure adoption and real-world impact.
• Maintain and prioritise development backlogs in partnership with cross-functional stakeholders.
• Contribute to internal capability uplift by providing technical leadership and promoting best practices in AI and data science.

About You

You may come from asset management systems, reliability engineering, operational analytics, or applied data science. Regardless of background, you bring strong analytical capability and the ability to translate operational challenges into scalable AI solutions.

Experience & Skills:

• 5+ years' experience in data science, AI engineering, analytics, or machine learning within asset-intensive or operational environments.
• Tertiary qualification in engineering, computer science, mathematics, or related discipline (or equivalent practical experience).
• Strong hands-on experience with Python and modern data science tooling.
• Demonstrated experience applying statistical modelling, machine learning, causal inference, and experimental design in operational contexts.
• Experience with generative AI technologies, including LLMs, AI agents, embedding techniques, and performance evaluation frameworks.
• Proven ability to work in cross-functional teams spanning business, engineering, and technology.
• Strong communication skills, with the ability to translate complex technical concepts into clear, actionable requirements.