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- Tasks: Create scalable machine learning models for personalised travel recommendations.
- Company: Global travel tech firm with a focus on innovation.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Why this job: Join a dynamic team and make travel smarter with your ML expertise.
- Qualifications: Ph.D. or Master's in a relevant field, plus Python and ML library skills.
- Other info: Collaborative environment tackling complex challenges.
The predicted salary is between 36000 - 60000 £ per year.
A global travel technology firm is seeking a Machine Learning Scientist II to develop scalable machine learning models for personalized recommendations. The role involves contributions from data preprocessing to deployment and requires a Ph.D. or Master's in a relevant field, along with proficiency in Python and ML libraries. Ideal candidates should have strong problem-solving skills and enthusiasm for tackling complex challenges in a collaborative environment.
ML Scientist II: Personalization at Scale in Westminster employer: Expedia, Inc.
Contact Detail:
Expedia, Inc. Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land ML Scientist II: Personalization at Scale in Westminster
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. We all know that sometimes it’s not just what you know, but who you know that can help you land that dream job.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects. We recommend including any personalisation models you've developed. This will give potential employers a taste of what you can do!
✨Tip Number 3
Prepare for those interviews! Brush up on your Python and ML libraries, and be ready to discuss your problem-solving approach. We suggest practicing common interview questions with friends or using mock interview platforms.
✨Tip Number 4
Apply through our website! We make it easy for you to find roles that match your skills. Plus, it shows you're genuinely interested in joining our team. Don’t miss out on the chance to work with us on exciting projects!
We think you need these skills to ace ML Scientist II: Personalization at Scale in Westminster
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your experience with Python and ML libraries in your application. We want to see how you've tackled complex problems in the past, so don’t hold back on showcasing your technical prowess!
Tailor Your Application: Take a moment to customise your CV and cover letter for this role. We love seeing candidates who understand our needs and can demonstrate how their background aligns with developing scalable machine learning models for personalised recommendations.
Be Clear and Concise: When writing your application, keep it clear and to the point. We appreciate well-structured responses that get straight to the heart of your experiences and skills. Remember, less is often more!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it’s super easy to do!
How to prepare for a job interview at Expedia, Inc.
✨Know Your ML Models Inside Out
Make sure you’re well-versed in the machine learning models relevant to the role. Be prepared to discuss your experience with developing scalable models and how you've tackled challenges in the past. Brush up on the latest trends in personalisation algorithms, as this will show your enthusiasm for the field.
✨Showcase Your Python Proficiency
Since proficiency in Python is a must, be ready to demonstrate your coding skills. You might be asked to solve a problem on the spot or explain your thought process while coding. Practise common ML libraries like TensorFlow or PyTorch, and be prepared to discuss how you've used them in previous projects.
✨Prepare for Problem-Solving Questions
Expect questions that assess your problem-solving abilities. Think of complex challenges you've faced and how you approached them. Use the STAR method (Situation, Task, Action, Result) to structure your answers, making it easier for the interviewer to follow your thought process.
✨Emphasise Collaboration Skills
This role requires working in a collaborative environment, so highlight your teamwork experiences. Share examples of how you’ve successfully collaborated with others to achieve a common goal, especially in data-driven projects. This will demonstrate that you can thrive in their team-oriented culture.
