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- Tasks: Design and enhance machine learning solutions for personalised recommendations.
- Company: Leading online marketplace in Greater London with a focus on innovation.
- Benefits: Flexible working, comprehensive health benefits, and a supportive culture.
- Why this job: Join a team that values creativity and makes a real impact on user experience.
- Qualifications: Experience in deep learning and Python, with a passion for innovation.
- Other info: Great opportunities for learning and professional development.
The predicted salary is between 36000 - 60000 £ per year.
A leading online marketplace in Greater London is seeking a Machine Learning Scientist to enhance personalization through advanced recommendation systems.
Responsibilities include:
- Designing machine learning solutions
- Collaborating cross-functionally
- Conducting experiments to improve models
Ideal candidates will have experience in deep learning and Python, with a passion for innovation.
This position offers flexible working, comprehensive health benefits, and a supportive culture focused on learning and development.
ML Scientist, Recommendations — Scale Personalised AI in London employer: Depop
Contact Detail:
Depop Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land ML Scientist, Recommendations — Scale Personalised AI in London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with fellow ML enthusiasts. 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 machine learning projects, especially those involving recommendation systems. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your deep learning knowledge and Python skills. Practice explaining your past projects and how they relate to the role. Confidence and clarity can make all the difference!
✨Tip Number 4
Don’t forget to apply through our website! We love seeing candidates who are genuinely interested in joining our team. Tailor your application to highlight your passion for innovation and collaboration in ML.
We think you need these skills to ace ML Scientist, Recommendations — Scale Personalised AI in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in deep learning and Python. We want to see how your skills align with the role of an ML Scientist, so don’t be shy about showcasing relevant projects or achievements!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to express your passion for innovation and how you can contribute to enhancing our recommendation systems. Let us know why you’re excited about this opportunity!
Showcase Collaboration Skills: Since this role involves cross-functional collaboration, mention any experiences where you’ve worked with different teams. We love seeing candidates who can communicate effectively and work well with others!
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 don’t miss out on any important updates from our team!
How to prepare for a job interview at Depop
✨Know Your ML Fundamentals
Brush up on your machine learning fundamentals, especially around recommendation systems. Be ready to discuss algorithms you've used and the impact they had on personalisation. This shows your depth of knowledge and passion for the field.
✨Showcase Your Python Skills
Prepare to demonstrate your Python expertise. You might be asked to solve a coding problem or explain how you've used Python in past projects. Practising common libraries like TensorFlow or PyTorch can give you an edge.
✨Collaborate Like a Pro
Since the role involves cross-functional collaboration, think of examples where you've worked with different teams. Highlight your communication skills and how you’ve successfully navigated challenges in team settings.
✨Be Ready to Experiment
Expect questions about your approach to conducting experiments and improving models. Prepare to discuss specific experiments you've run, what you learned from them, and how you iterated on your models based on those insights.
