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- Tasks: Develop ranking algorithms and analyse datasets to improve user experience.
- Company: Global luxury fashion marketplace with a focus on innovation.
- Benefits: Dynamic work environment, competitive rewards, and professional growth opportunities.
- Why this job: Join a creative team and make a real impact in the fashion industry.
- Qualifications: Strong background in Machine Learning and Python, plus teamwork skills.
- Other info: Collaborative culture with exciting projects and career advancement.
The predicted salary is between 36000 - 60000 £ per year.
A global luxury fashion marketplace is seeking a highly motivated Data Scientist to join their Search & Rankings team. The role focuses on developing Ranking algorithms and deeply analyzing datasets to enhance user experience.
Candidates should have a strong background in Machine Learning and Python, coupled with a collaborative mindset to work effectively with engineers and teams across the organization. This position offers a dynamic work environment and various rewards and benefits.
Data Scientist - Search & Ranking (LTR/IR) in London employer: Farfetch
Contact Detail:
Farfetch Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist - Search & Ranking (LTR/IR) in London
✨Tip Number 1
Network like a pro! Reach out to current employees in the Search & Rankings team on LinkedIn. A friendly chat can give you insider info and might just get your foot in the door.
✨Tip Number 2
Show off your skills! Prepare a mini-project or case study that showcases your expertise in Machine Learning and Python. Bring it up during interviews to demonstrate your hands-on experience.
✨Tip Number 3
Be ready to collaborate! Brush up on your teamwork skills, as this role requires working closely with engineers. Think of examples where you've successfully collaborated in the past.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who take the initiative to connect directly with us.
We think you need these skills to ace Data Scientist - Search & Ranking (LTR/IR) in London
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your experience with Machine Learning and Python in your application. We want to see how you've used these skills in real-world scenarios, so don’t hold back!
Tailor Your Application: Take a moment to customise your CV and cover letter for this role. Mention specific projects or experiences that relate to developing ranking algorithms or analysing datasets. It shows us you’re genuinely interested!
Be Collaborative: Since we value teamwork, share examples of how you've worked with engineers or cross-functional teams in the past. This will help us see how you fit into our dynamic work environment.
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 us!
How to prepare for a job interview at Farfetch
✨Know Your Algorithms
Brush up on your knowledge of ranking algorithms and machine learning techniques. Be prepared to discuss how you've applied these in past projects, as well as any challenges you faced and how you overcame them.
✨Showcase Your Python Skills
Since Python is crucial for this role, make sure you can demonstrate your coding skills. Consider preparing a small project or example that highlights your ability to manipulate datasets and implement algorithms effectively.
✨Collaborative Mindset
This position requires working closely with engineers and other teams. Be ready to share examples of how you've successfully collaborated in the past, and express your enthusiasm for teamwork and cross-functional projects.
✨Understand the User Experience
Since the role focuses on enhancing user experience, think about how data science can impact this area. Prepare to discuss how your work can improve search results and rankings, and be ready to suggest innovative ideas during the interview.
