Role overview
As a senior member of the team, you will lead the development and delivery of advanced analytics solutions, working at the intersection of data science, engineering, and domain expertise. You will take ownership of high‑impact modelling initiatives, applying state‑of‑the‑art machine‑learning and signal‑processing techniques to complex, real‑world datasets.
You will work with high‑frequency vibration data, SCADA data, and turbine failure records to build models that directly influence operational decision‑making for wind‑farm owners and operators.
This is a hands‑on, delivery‑focused role with significant autonomy, where you will be expected to drive solutions from concept through to production‑ready deployment.
Responsibilities
- Lead the design, development and optimisation of advanced AI/ML models to detect, diagnose and predict wind‑turbine failure modes
- Apply sophisticated signal‑processing, reliability engineering and statistical techniques to large‑scale operational datasets
- Develop probabilistic models to estimate remaining useful life (RUL) and component failure risk
- Take ownership of end‑to‑end model lifecycle, including validation, testing and deployment into production environments
- Translate complex analytical outputs into clear, commercially relevant insights for engineering and operational stakeholders
- Mentor and collaborate with data scientists and engineers, promoting best practice in modelling and responsible AI
- Contribute to the continuous improvement of analytics capabilities, tooling and methodologies within the team
About the company
- 5+ years' experience in Data Science, Machine Learning or a related field, with a strong track record of delivering models in production environments
- Advanced Python skills and deep experience with ML frameworks (e.g. scikit‑learn, TensorFlow, PyTorch)
- Strong foundation in statistics, probabilistic modelling, and/or signal processing
- Experience working with complex industrial or time‑series datasets at scale
- Demonstrated ability to operate autonomously, prioritise effectively, and deliver against ambiguous or evolving requirements
- Confident communicator, able to translate complex technical concepts into clear, actionable insights for non‑technical stakeholders
- Experience in wind energy, condition monitoring, rotating machinery, or reliability engineering
- Exposure to cloud‑based ML deployment and productionisation pipelines
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