At a Glance
- Tasks: Build and maintain scalable data pipelines and deploy machine learning models.
- Company: Join a growing tech team in Newcastle with a focus on innovation.
- Benefits: Earn £40,000-£55,000, enjoy hybrid working, and receive extensive training.
- Other info: Collaborative environment with opportunities for continuous career progression.
- Why this job: Make a real impact by turning complex data into intelligent solutions.
- Qualifications: Experience in data engineering, strong Python and SQL skills required.
The predicted salary is between 40000 - 55000 € per year.
We're looking for a Data & Machine Learning Engineer to join a growing technology team in Newcastle. This role offers the chance to work on modern data platforms and machine learning solutions, helping organisations turn complex data into intelligent, real-world outcomes.
What you'll have the opportunity to do as a Data & ML Engineer:
- Building and maintaining scalable data pipelines that support analytics and machine learning
- Developing and deploying machine learning models into production environments
- Working with cloud-based data platforms to ingest, process and transform data at scale
- Collaborating with engineers, analysts and stakeholders to deliver end-to-end solutions
- Supporting good engineering practice across testing, automation and deployment
- Contributing to Agile delivery through teamwork and continuous improvement
About the Candidate:
The right Data & ML Engineer should have:
- Experience in data engineering, machine learning or applied AI roles
- Strong skills in Python and SQL, with exposure to big data or ML frameworks
- Experience working with cloud platforms such as Azure, AWS or GCP
- Understanding of CI/CD, MLOps or infrastructure-as-code practices
- Confidence communicating technical concepts to non-technical audiences
- Eligible for SC clearance (5+ years continuous UK residency)
What's in it for you?
- Salary from £40,000 to £55,000
- Hybrid working based in Newcastle
- 25 Days holiday per annum
- Extensive benefits package
- Continuous training provided for career progression
To hear more about the Data & Machine Learning Engineer role contact Daire McIlhatton.
Data & Machine Learning Engineer in Newcastle upon Tyne employer: Anson McCade
Join a forward-thinking technology team in Newcastle as a Data & Machine Learning Engineer, where you will have the opportunity to work on cutting-edge data platforms and machine learning solutions. With a strong emphasis on hybrid working, a generous benefits package, and continuous training for career progression, this role offers a supportive and collaborative work culture that values innovation and teamwork. Experience the unique advantage of being part of a growing organisation that is committed to turning complex data into intelligent outcomes while enjoying a balanced work-life environment.
StudySmarter Expert Advice🤫
We think this is how you could land Data & Machine Learning Engineer in Newcastle upon Tyne
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at local meetups. We can’t stress enough how personal connections can lead to job opportunities that aren’t even advertised.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data pipelines and machine learning models. We all love a good project, and having something tangible to discuss can really set you apart in interviews.
✨Tip Number 3
Prepare for those technical interviews! Brush up on your Python and SQL skills, and be ready to tackle some coding challenges. We recommend practicing with real-world problems to get into the right mindset.
✨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’re always looking for passionate candidates who want to make an impact in the tech world.
We think you need these skills to ace Data & Machine Learning Engineer in Newcastle upon Tyne
Some tips for your application 🫡
Tailor Your CV:Make sure your CV speaks directly to the Data & Machine Learning Engineer role. Highlight your experience with Python, SQL, and any cloud platforms you've worked with. We want to see how your skills align with what we're looking for!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about data engineering and machine learning. Share specific examples of projects you've worked on that relate to the job description. We love a good story!
Show Off Your Projects:If you've got any personal or professional projects that showcase your skills in building data pipelines or deploying ML models, include them! We appreciate hands-on experience and want to see what you've been up to in the tech world.
Apply Through Our Website:Don't forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it makes the whole process smoother for everyone involved!
How to prepare for a job interview at Anson McCade
✨Know Your Tech Inside Out
Make sure you brush up on your Python and SQL skills before the interview. Be ready to discuss your experience with big data and machine learning frameworks, as well as any cloud platforms like Azure, AWS, or GCP you've worked with.
✨Showcase Your Problem-Solving Skills
Prepare to share specific examples of how you've built and maintained scalable data pipelines or deployed machine learning models. Use the STAR method (Situation, Task, Action, Result) to structure your answers and highlight your contributions.
✨Communicate Clearly
Since you'll need to explain technical concepts to non-technical audiences, practice simplifying complex ideas. Think about how you can convey your past projects in a way that anyone can understand, focusing on the impact of your work.
✨Embrace Agile Mindset
Familiarise yourself with Agile principles and be prepared to discuss how you've contributed to teamwork and continuous improvement in previous roles. Highlight any experience you have with CI/CD, MLOps, or infrastructure-as-code practices.
