At a Glance
- Tasks: Design and implement machine learning models to predict faults in wind turbines.
- Company: Join a dynamic team of data scientists and engineers tackling real-world challenges.
- Benefits: Competitive pay, hands-on experience, and the chance to work on impactful projects.
- Other info: Fast-paced environment with opportunities for growth and innovation.
- Why this job: Make a difference in renewable energy while honing your data science skills.
- Qualifications: 3+ years in data science, strong Python skills, and experience with ML frameworks.
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 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 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.
Data Scientist in Nottingham employer: Opus Recruitment Solutions
Join a forward-thinking team dedicated to harnessing the power of data in the renewable energy sector. Our collaborative work culture fosters innovation and growth, providing you with hands-on experience in applying machine learning techniques to real-world challenges. With opportunities for professional development and a focus on meaningful contributions to sustainability, this role offers a rewarding career path in a dynamic environment.
StudySmarter Expert Advice🤫
We think this is how you could land Data Scientist in Nottingham
✨Tip Number 1
Network like a pro! Reach out to your connections in the data science field, especially those who work with machine learning or in the wind energy sector. A friendly chat can lead to opportunities that aren’t even advertised yet.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving Python and machine learning. This is your chance to demonstrate how you’ve tackled real-world datasets and delivered insights.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. Be ready to explain complex concepts in simple terms, as you’ll need to communicate effectively with non-technical teams.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who take the initiative to connect directly with us.
We think you need these skills to ace Data Scientist in Nottingham
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your experience in data science, especially with Python and machine learning frameworks. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects or achievements!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you’re passionate about data science and how your background fits into our multidisciplinary team. Keep it concise but engaging – we love a good story!
Showcase Real-World Experience:If you've worked with messy datasets or in industrial settings, make sure to mention that! We’re looking for hands-on experience, so share specific examples of how you’ve tackled complex problems in the past.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way to ensure your application gets seen by the right people. Plus, it shows us you’re serious about joining our team!
How to prepare for a job interview at Opus Recruitment Solutions
✨Know Your Data Inside Out
Before the interview, dive deep into the types of datasets mentioned in the job description. Familiarise yourself with high-frequency vibration signals and SCADA data. Being able to discuss your experience with similar datasets will show that you understand the complexities involved.
✨Showcase Your Python Skills
Make sure to highlight your proficiency in Python, especially with libraries like NumPy, pandas, and SciPy. Prepare examples of how you've used these tools in past projects, particularly in building and testing machine learning models. This will demonstrate your hands-on experience.
✨Communicate Clearly
Since you'll need to convey technical findings to non-technical audiences, practice explaining complex concepts in simple terms. Think of a few examples where you've successfully done this before, as it will be crucial for working with engineering and operational teams.
✨Prepare for Real-World Scenarios
Expect questions about how you would approach real-world problems, especially in the context of wind energy and predictive maintenance. Be ready to discuss your thought process and any relevant experiences, as this will showcase your problem-solving skills and adaptability.
