Principal Data Scientist - Simulation & Techno-Economic Optimisation

About Us

Fortescue is both a proud West Australian company and a global green solutions business. We are recognised for our culture, innovation and industry-leading development of infrastructure, mining assets and green energy initiatives.

Our Opportunity

Work Location: Perth – Fortescue’s Perth office is located on the traditional lands of the Whadjuk Noongar people

Roster: Monday to Friday 

As a Principal Data Scientist, you will play a pivotal role in advancing Fortescue’s renewable energy and green iron initiatives. You will lead the design, development, and application of simulation-driven machine learning approaches that optimise techno-economic performance across large-scale renewable and green industrial projects. This role combines advanced data science expertise, domain knowledge in energy systems, and leadership to deliver insights that directly inform strategic decisions and project design.

You will collaborate closely with Engineering, Projects, Technology, and Commercial teams to ensure our modelling frameworks deliver accurate, scalable, and actionable outcomes that accelerate the decarbonisation journey.

Key Responsibilities

• Lead development of advanced simulation and ML-driven frameworks for renewable energy and green iron
• Mentor and guide teams in modelling, data quality, and uncertainty management
• Translate technical insights into clear recommendations for senior leaders
• Create hybrid models combining simulation, optimisation, and machine learning
• Apply ML to speed up simulations, identify cost drivers, and optimise systems
• Build models linking renewable energy, hydrogen, electrolysers, and green iron production
• Integrate engineering, cost, and financial data into unified decision-support tools
• Run scenario analyses and sensitivity studies to guide investment and delivery
• Drive innovation with digital twins, surrogate models, reinforcement learning, and partnerships
• Collaborate across teams, promoting transparency and communicating results effectively.

Qualifications and Experience

• PhD or Master’s degree in Data Science, Engineering, Applied Mathematics, Energy Systems, or a related field
• 8+ years of experience in advanced data science, machine learning, or computational modelling, ideally in energy, resources, or process industries
• Proven expertise in simulation-based optimisation, surrogate modelling, and large-scale data integration
• Strong proficiency in C++, Python strongly preferred
• Strong experience with ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn, OpenAI).
• Familiarity with AWS desired
• Demonstrated experience linking engineering/physical models with techno-economic analysis.
• Familiarity with renewable energy systems, hydrogen, or metallurgical processes (advantageous).
• Excellent leadership, collaboration, and communication skills, with the ability to influence and align diverse stakeholders.

Our Commitment

Fortescue is deeply committed to providing a safe culture that builds respect, fosters inclusiveness, and values diversity. We celebrate individual strengths and team members from all backgrounds are encouraged to bring their whole selves to work. Our global workforce drives and promotes an inclusive culture, both within our organisation and throughout the communities we interact with across the world. Diverse backgrounds include First Nations Peoples, people with differing abilities, LGBTIQ+ community, gender, neurodiverse, cultural diversity, all age groups, and those with an intersectional or multiple diverse characteristics. We encourage candidates from all backgrounds to apply.

https://fortescue.com/careers 

Internal Candidates / Current Contractors please apply via Success Factors Careers Portal. For further information on how to apply please visit the Fortescue Hub. 

Fortescue reserves the right to close applications early should a suitable pool of candidates be identified. Fortescue will never contact you to ask for payment of any kind, whether directly or through a third party.