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- Tasks: Develop predictive models using time series and sensor data for real-world utilities challenges.
- Company: Join a tech-focused company dedicated to innovative solutions in the utilities sector.
- Benefits: Enjoy a collaborative environment with opportunities for continuous learning and growth.
- Why this job: Make a real impact while working on exciting machine learning projects with a supportive team.
- Qualifications: 3+ years in data analytics, strong Python skills, and a relevant degree required.
- Other info: Ideal for those eager to tackle meaningful problems in a dynamic start-up atmosphere.
The predicted salary is between 36000 - 60000 £ per year.
Enso Recruitment is working on behalf of a client in the technology sector to hire a Machine Learning Engineer with a strong background in data analytics. This role offers the chance to apply advanced ML techniques to real-world challenges within the utilities sector, delivering meaningful outcomes through predictive analytics. The successful candidate will join a growing data and modelling team, contributing to a cloud-based platform used to monitor and forecast network performance. This is a role for someone who enjoys exploring complex datasets, designing scalable data processes, and integrating intelligent features into production systems.
Key Responsibilities:- Work with time series and sensor data to develop predictive models
- Carry out exploratory data analysis and visualisation to uncover insights
- Collaborate with domain specialists to guide modelling strategies
- Lead integration of ML models into the main product, focusing on long-term maintainability
- Engage with customers to understand needs and present findings
- Contribute to continuous deployment and delivery pipelines
- Participate in testing, implementation and live deployment of new features
- Support team-wide development through code sharing and feedback
- Over 3 years of practical experience in data analytics and machine learning
- Strong Python skills, with experience using libraries like Pandas and Scikit-learn (or similar)
- Familiarity with data exploration tools and visualisation methods
- A relevant third-level qualification (e.g. Computer Science, Data Science, Software Engineering)
- Ability to communicate clearly with technical and non-technical stakeholders
- A team-oriented mindset and readiness to adapt in a start-up style environment
- A focus on quality, reliability and continuous improvement
- Understanding of MLOps concepts and workflows
- Experience with geospatial (GIS) data
- Background in agile development practices
- Familiarity with AWS and automation/testing tools
- Experience in CI/CD pipelines and deployment automation
This role would suit someone who is not only technically capable but also driven to work on meaningful problems. You’ll be given ownership of your work while collaborating closely with a wider development team, with opportunities to deepen your knowledge in machine learning and AI. If you’re looking for a position that balances technical challenge with real-world impact, we’d love to hear from you.
Machine Learning Engineer (Belfast) employer: Enso Recruitment
Contact Detail:
Enso Recruitment Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer (Belfast)
✨Tip Number 1
Familiarise yourself with the specific tools and technologies mentioned in the job description, such as Python libraries like Pandas and Scikit-learn. Having hands-on experience with these tools will not only boost your confidence but also demonstrate your readiness for the role.
✨Tip Number 2
Engage with the machine learning community online. Participate in forums, attend webinars, or join local meetups to discuss predictive analytics and data modelling. This can help you gain insights into industry trends and may even lead to networking opportunities that could benefit your application.
✨Tip Number 3
Prepare to discuss real-world applications of your work during interviews. Think of specific projects where you've applied machine learning techniques to solve problems, especially those involving time series or sensor data, as this aligns closely with the responsibilities of the role.
✨Tip Number 4
Showcase your ability to communicate complex ideas clearly. Practice explaining your past projects to both technical and non-technical audiences. This skill is crucial for collaborating with domain specialists and engaging with customers, which are key aspects of the job.
We think you need these skills to ace Machine Learning Engineer (Belfast)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in data analytics and machine learning. Emphasise your proficiency in Python and relevant libraries like Pandas and Scikit-learn, as well as any experience with predictive modelling and data visualisation.
Craft a Compelling Cover Letter: In your cover letter, express your passion for solving real-world problems through machine learning. Mention specific projects or experiences that demonstrate your ability to work with complex datasets and collaborate with teams.
Showcase Relevant Projects: If you have worked on projects involving time series data or predictive models, be sure to include these in your application. Describe your role, the tools you used, and the outcomes of your work to illustrate your capabilities.
Prepare for Technical Questions: Anticipate technical questions related to machine learning concepts and data analytics during the interview process. Brush up on MLOps, CI/CD pipelines, and any relevant tools or methodologies to demonstrate your expertise.
How to prepare for a job interview at Enso Recruitment
✨Showcase Your Technical Skills
Be prepared to discuss your experience with Python and relevant libraries like Pandas and Scikit-learn. Bring examples of projects where you've applied machine learning techniques, especially in data analytics.
✨Demonstrate Problem-Solving Abilities
Think of specific instances where you've tackled complex datasets or developed predictive models. Be ready to explain your thought process and the impact of your solutions on real-world challenges.
✨Communicate Effectively
Practice explaining technical concepts in a way that non-technical stakeholders can understand. This will show your ability to bridge the gap between technical and non-technical team members.
✨Emphasise Team Collaboration
Highlight your experience working in teams, especially in agile environments. Discuss how you’ve contributed to code sharing, feedback, and continuous improvement within a team setting.
