Description

Salary: £104,000 - 117,000 per year

• Around 3 years experience in a data science or similar role
• Strong Python skills, particularly with libraries like NumPy, pandas, and SciPy
• Experience with ML frameworks such as scikit-learn, TensorFlow, or PyTorch
• A track record of working with messy, real-world datasets, ideally in industrial settings
• Comfortable operating in fast-paced environments with some ambiguity
• Able to communicate technical findings clearly to non-technical audiences
• Experience in wind energy, rotating equipment, predictive maintenance, or reliability engineering would be a big advantage, but is not essential

• Designing and refining machine learning models to identify, diagnose, and predict faults in wind turbines
• Using a combination of signal processing, machine learning, and reliability-focused approaches on operational data
• Developing probabilistic models to assess component health and predict remaining useful life
• Turning complex outputs into clear, usable insights for engineering and operational teams
• Working closely with data and engineering teams to help move models into live environments
• Supporting validation, testing, and responsible use of AI solutions

• AI
• Machine Learning
• PyTorch
• Python
• TensorFlow
• numpy
• pandas

More:

We are a multidisciplinary team of data scientists, ML engineers, software developers, and subject-matter experts working together to solve complex problems using advanced analytics. This is a hands-on role focused on applying machine learning to large-scale, real-world datasets, including high-frequency vibration signals, SCADA data, and historical turbine failure records. We are building, testing, and implementing models that deliver meaningful, practical insights for wind farm operators. The role is outside IR35, runs for 6 months, and offers a day rate of £400 to £450.

last updated 24 week of 2026