Data Scientist

Data Scientist | Outside IR35 | 6 Months | £400 - £450 |

You’ll be joining a multidisciplinary team made up of data scientists, ML engineers, software developers, and subject-matter experts, working together to solve complex problems using advanced analytics. The focus is on applying machine learning techniques to large-scale, real-world datasets — including high-frequency vibration signals, SCADA data, and historical turbine failure records.

This is a very hands-on role, centred around building, testing, and implementing models that deliver meaningful, practical insights for wind farm operators.

What you’ll be doing :
Designing and refining machine learning models to identify, diagnose, and predict faults in wind turbines
Using a combination of signal processing, ML, 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

What we’re looking for:

Around 3+ years’ experience in a data science or similar role
Strong Python skills, particularly with libraries like Num Py, pandas, and Sci Py

Experience with ML frameworks such as scikit-learn, Tensor Flow, or Py Torch
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 areas like wind energy, rotating equipment, predictive maintenance, or reliability engineering would be a big advantage — but it’s not essential.

If this is a role that suits your skill set  and available immediately then please apply for the job advert directly or reach out to me with your CV to (url removed).

Data Scientist | Outside IR35 | 6 Months | £400 - £450