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- Tasks: Build and deploy machine learning models in live pricing systems using Python.
- Company: Join a leading analytics recruitment agency with a focus on innovation.
- Benefits: Gain hands-on experience, mentorship from experts, and opportunities for technical growth.
- Why this job: Make a real impact on business performance while working with cutting-edge machine learning technology.
- Qualifications: Strong Python skills and a passion for machine learning in practical settings.
- Other info: Collaborative environment with a focus on learning and development.
The predicted salary is between 28800 - 48000 £ per year.
This is an exciting opportunity to build a career in production machine learning within a large scale pricing and analytics environment. You will join a growing Machine Learning Operations team at the heart of a major transformation programme. The focus is on modernising how pricing models are built, tested and deployed into live systems. This role is ideal if you enjoy Python, problem solving and want to understand how machine learning works in real business settings rather than just notebooks. This is not a research focused role and it is not purely infrastructure. The impact happens in the middle, where models become reliable, scalable and ready for customers.
Responsibilities
- Working on Python based rating and machine learning deployments used in live pricing systems
- Supporting testing and analysis to ensure changes are accurate, controlled and high quality
- Building and improving tools, frameworks and APIs that help teams deploy models with confidence
- Collaborating with engineers, analysts and stakeholders to turn ideas into working solutions
- Contributing to clear and well-structured technical documentation
- Developing an understanding of how machine learning impacts customer pricing and business performance
Qualifications
- A genuine interest in machine learning systems and how models move into production
- Strong Python fundamentals and a desire to grow your engineering capability
- A degree in a mathematical or technical subject or equivalent practical experience
- Logical thinking and a structured approach to problem solving
- Curiosity, initiative and a willingness to learn
- Clear communication skills and comfort working as part of a collaborative team
You will work on real systems that matter, not isolated exercises. You will be supported by experienced engineers, exposed to modern tooling, and given the space to build confidence in production machine learning and ML Operations. This role is designed to set strong foundations for long term technical growth.
Compliance
Right to work: You must be eligible and authorised to work in the United Kingdom.
ML Operations Engineer (Python) in Manchester employer: Datatech Analytics
Contact Detail:
Datatech Analytics Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land ML Operations Engineer (Python) in Manchester
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups or webinars, and connect with professionals on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your Python projects, especially those related to machine learning. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on common ML Ops questions and practical scenarios. Practice explaining your thought process clearly, as communication is key when collaborating with teams and stakeholders.
✨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 are proactive about their job search!
We think you need these skills to ace ML Operations Engineer (Python) in Manchester
Some tips for your application 🫡
Show Your Python Passion: Make sure to highlight your Python skills in your application. We want to see how you've used Python in real-world scenarios, especially in machine learning contexts. Share specific projects or experiences that showcase your coding prowess!
Be Clear and Concise: When writing your application, clarity is key! We appreciate well-structured responses that get straight to the point. Avoid jargon unless it's necessary, and make sure your enthusiasm for the role shines through.
Demonstrate Problem-Solving Skills: We love a good problem solver! In your application, include examples of challenges you've faced and how you tackled them, particularly in machine learning or engineering contexts. This will show us your logical thinking and structured approach.
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 gives you a chance to explore more about what we do at StudySmarter!
How to prepare for a job interview at Datatech Analytics
✨Know Your Python Inside Out
Make sure you brush up on your Python skills before the interview. Be ready to discuss your experience with Python in detail, especially how you've used it in machine learning contexts. Practise coding challenges that focus on Python to demonstrate your problem-solving abilities.
✨Understand Machine Learning in Real Business
Familiarise yourself with how machine learning models are deployed in production environments, particularly in pricing systems. Be prepared to discuss any relevant projects or experiences where you've seen machine learning impact business performance. This will show your genuine interest in the field.
✨Collaboration is Key
Since this role involves working closely with engineers and analysts, think of examples from your past experiences where teamwork led to successful outcomes. Highlight your communication skills and how you’ve contributed to collaborative projects, as this will resonate well with the interviewers.
✨Prepare Questions About the Role
Have a few thoughtful questions ready about the ML Operations team and their current projects. This shows your enthusiasm for the role and helps you gauge if it's the right fit for you. Ask about the tools they use or how they measure the success of their models in production.
