Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Enhance pricing processes using innovative machine learning models and automation.
- Company: Leading technology insurance firm in Greater London with a focus on innovation.
- Benefits: Competitive salary, flexible working conditions, and opportunities for growth.
- Why this job: Join a dynamic team to revolutionise pricing strategies with cutting-edge technology.
- Qualifications: Experience in ML lifecycle management and proficiency in Python and cloud environments.
The predicted salary is between 48000 - 72000 £ per year.
A leading technology insurance firm in Greater London is looking for a Lead Machine Learning Engineer to enhance pricing processes through innovative models. The role focuses on optimising and automating processes, developing model lifecycle tooling, and collaborating with Data Scientists and Product teams.
Ideal candidates should have experience in ML lifecycle management and a solid understanding of Python and cloud environments. Competitive salary and flexible working conditions are offered.
Lead ML Engineer – Pricing Automation & Scale in London employer: Zego
Contact Detail:
Zego Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Lead ML Engineer – Pricing Automation & Scale in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those working in ML or at tech insurance firms. A friendly chat can lead to insider info about job openings that aren't even advertised yet.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your ML projects, especially those related to pricing automation. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for interviews by brushing up on your Python and cloud environment knowledge. Be ready to discuss how you've optimised processes in the past and how you can contribute to their team.
✨Tip Number 4
Don't forget to apply through our website! We make it easy for you to find roles that match your skills and interests. Plus, it shows you're serious about joining our team!
We think you need these skills to ace Lead ML Engineer – Pricing Automation & Scale in London
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your experience with ML lifecycle management and your Python prowess. We want to see how you can bring your skills to the table, so don’t hold back!
Tailor Your Application: Take a moment to customise your application for this role. Mention how your past experiences align with optimising and automating processes, as that’s what we’re all about at StudySmarter.
Be Clear and Concise: When writing your application, keep it straightforward. We appreciate clarity, so make sure your points are easy to understand and get straight to the point.
Apply Through Our Website: Don’t forget to submit your application through our website! It’s the best way for us to receive your details and ensures you’re in the running for this exciting opportunity.
How to prepare for a job interview at Zego
✨Know Your ML Lifecycle
Make sure you can confidently discuss the machine learning lifecycle. Be prepared to explain how you've managed models from conception to deployment, and share specific examples of tools or frameworks you've used in previous projects.
✨Showcase Your Python Skills
Brush up on your Python knowledge, especially libraries relevant to machine learning like TensorFlow or scikit-learn. You might be asked to solve a coding problem or discuss your approach to a specific ML challenge, so practice articulating your thought process.
✨Understand Pricing Automation
Familiarise yourself with pricing strategies and how machine learning can enhance these processes. Be ready to discuss innovative models you've developed or researched that could apply to pricing automation, demonstrating your industry knowledge.
✨Collaboration is Key
Since this role involves working closely with Data Scientists and Product teams, prepare to talk about your experience in collaborative environments. Share examples of how you've successfully worked with cross-functional teams to achieve project goals.
