About the Position:
We are seeking a Senior Energy Systems Data Scientist to lead our team in advancing renewable energy and green iron initiatives. As a key member of our data science function, you will play a pivotal role in driving innovation and delivering insights that inform strategic decisions and project design.
* Develop advanced simulation and machine learning-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 machine learning 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.
Requirements:
* A Doctoral 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++ and Python.
* Experience with ML frameworks such as TensorFlow, PyTorch, scikit-learn, and 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.
Benefits:
* A safe culture that builds respect, fosters inclusiveness, and values diversity.
* We celebrate individual strengths and encourage team members from all backgrounds to bring their whole selves to work.