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- Tasks: Build features for data-driven pricing strategies and ensure system reliability throughout the ML lifecycle.
- Company: Innovative software company revolutionising hospitality and revenue management.
- Benefits: Remote work, competitive salary, and opportunities for professional growth.
- Why this job: Join a dynamic team and make a real impact in optimising revenue for hotels.
- Qualifications: Bachelor's degree and 3+ years of relevant experience in MLOps.
- Other info: Expected travel to Paddington two days a week.
The predicted salary is between 36000 - 60000 £ per year.
A software company in the UK is seeking a Machine Learning Ops Engineer to build features for data-driven pricing strategies. The role focuses on ensuring system reliability and quality throughout the ML lifecycle.
Ideal candidates have a Bachelor's degree and 3+ years of relevant experience. This position is remote with expected travel into Paddington two days per week.
Join a dynamic, remote team dedicated to revolutionizing hospitality and optimizing revenue management for hotels.
Remote MLOps Engineer — Revenue Optimization in London employer: Cloudbeds
Contact Detail:
Cloudbeds Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Remote MLOps Engineer — Revenue Optimization in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or join relevant online communities. 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 MLOps projects, especially those related to revenue optimisation. 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 common MLOps scenarios and challenges. Think about how you would ensure system reliability and quality throughout the ML lifecycle, as this is key for the role.
✨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 Remote MLOps Engineer — Revenue Optimization in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in MLOps and any relevant projects you've worked on. We want to see how your skills align with our focus on data-driven pricing strategies!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Tell us why you're passionate about optimising revenue management and how you can contribute to our dynamic team. Keep it engaging and personal!
Showcase Your Technical Skills: Don’t forget to mention the specific tools and technologies you’ve used in your previous roles. We’re keen to know about your experience with ML lifecycle management and system reliability.
Apply Through Our Website: We encourage you to apply directly through our website for a smoother process. It helps us keep track of your application and ensures you don’t miss out on any updates from us!
How to prepare for a job interview at Cloudbeds
✨Know Your ML Lifecycle
Make sure you understand the entire machine learning lifecycle, from data collection to model deployment. Be ready to discuss how you've ensured system reliability and quality in your previous projects.
✨Showcase Your Problem-Solving Skills
Prepare examples of how you've tackled challenges in revenue optimisation or pricing strategies. Companies love candidates who can think critically and provide innovative solutions.
✨Familiarise Yourself with Remote Work Tools
Since this role is remote, be prepared to discuss your experience with collaboration tools like Slack, Zoom, or JIRA. Highlight how you’ve successfully worked in a remote team environment before.
✨Ask Insightful Questions
Prepare thoughtful questions about the company's approach to machine learning and revenue optimisation. This shows your genuine interest in the role and helps you assess if it's the right fit for you.
