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- Tasks: Lead data science projects to enhance customer engagement through machine learning.
- Company: Join Ralph Lauren, a global leader in premium lifestyle products.
- Benefits: Competitive salary, inclusive culture, and opportunities for professional growth.
- Why this job: Make a real impact on customer experiences with cutting-edge data science.
- Qualifications: Experience in machine learning and strong Python skills required.
- Other info: Collaborative environment with a focus on innovation and inclusion.
The predicted salary is between 36000 - 60000 £ per year.
Ralph Lauren Corporation (NYSE:RL) is a global leader in the design, marketing and distribution of premium lifestyle products in five categories: apparel, accessories, home, fragrances, and hospitality. For more than 50 years, Ralph Lauren's reputation and distinctive image have been consistently developed across an expanding number of products, brands and international markets. The Company's brand names, which include Ralph Lauren, Ralph Lauren Collection, Ralph Lauren Purple Label, Polo Ralph Lauren, Double RL, Lauren Ralph Lauren, Polo Ralph Lauren Children, Chaps, among others, constitute one of the world's most widely recognized families of consumer brands.
At Ralph Lauren, we unite and inspire the communities within our company as well as those in which we serve by amplifying voices and perspectives to create a culture of belonging, ensuring inclusion, and fairness for all. We foster a culture of inclusion through: Talent, Education & Communication, Employee Groups and Celebration.
We're looking for a passionate and experienced Data Scientist Manager to lead personalization efforts within Ralph Lauren's CRM ecosystem. You'll develop predictive models and recommendation systems that enhance customer engagement across global markets.
Essential Duties & Responsibilities- Lead development of machine learning solutions for CRM personalization.
- Build and optimize recommendation engines using neural networks and deep learnings, incorporating product embeddings and other advanced features to improve relevance and performance.
- Collaborate with CRM and regional marketing teams to align with campaign goals and customer segmentation strategies.
- Own the full ML lifecycle - from model design to deployment and monitoring.
- Partner with engineering and data teams to ensure scalable solutions.
- Continuously monitor and improve model performance using data insights and feedback.
- Proven experience in machine learning, particularly in recommendation systems and deep learning architectures.
- Strong understanding of two-tower neural networks, embedding techniques, and ranking models.
- Proficiency in Python with familiarity to ML libraries e.g. pandas, numpy, scipy, scikit-learn, tensorflow, pytorch.
- Familiarity with cloud platforms (GCP, AWS, Azure) and tools like Dataiku.
- Experience with ML Ops, including model deployment, monitoring, and retraining pipelines.
- Ability to work cross-functionally with marketing, CRM, and engineering teams.
- Excellent communication and stakeholder management skills.
- Experience in a global or multi-regional context is a plus.
Ralph Lauren Data Science Manager in London employer: Ralph Lauren
Contact Detail:
Ralph Lauren Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Ralph Lauren Data Science Manager in London
✨Tip Number 1
Network like a pro! Reach out to current or former employees at Ralph Lauren on LinkedIn. A friendly chat can give you insider info and maybe even a referral, which can really boost your chances.
✨Tip Number 2
Prepare for the interview by brushing up on your machine learning knowledge. Be ready to discuss your experience with recommendation systems and neural networks. We want to see your passion and expertise shine through!
✨Tip Number 3
Showcase your projects! If you've built any predictive models or recommendation engines, make sure to have them ready to discuss. Real-world examples can set you apart from other candidates.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you’re genuinely interested in joining the Ralph Lauren team.
We think you need these skills to ace Ralph Lauren Data Science Manager in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that align with the Data Science Manager role. Highlight your machine learning expertise, especially in recommendation systems, and don’t forget to mention any relevant projects or achievements!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to tell us why you’re passionate about data science and how your experience can contribute to Ralph Lauren's CRM personalization efforts. Be genuine and let your personality come through.
Showcase Your Technical Skills: Since this role requires proficiency in Python and ML libraries, make sure to include specific examples of your work with these technologies. If you've used cloud platforms like GCP or AWS, give us the details on how you leveraged them in your projects.
Apply Through Our Website: We encourage you to apply directly through our website for the best chance of getting noticed. It’s super easy, and you’ll be able to keep track of your application status. Plus, we love seeing applications come in through our own platform!
How to prepare for a job interview at Ralph Lauren
✨Know Your Machine Learning Stuff
Make sure you brush up on your machine learning knowledge, especially around recommendation systems and deep learning architectures. Be ready to discuss specific projects you've worked on, particularly those involving two-tower neural networks and embedding techniques.
✨Show Off Your Collaboration Skills
Ralph Lauren values teamwork, so be prepared to talk about how you've worked cross-functionally with marketing, CRM, and engineering teams in the past. Share examples of how you aligned your data science work with broader campaign goals and customer segmentation strategies.
✨Demonstrate Your Technical Proficiency
Familiarise yourself with the tools and libraries mentioned in the job description, like Python, TensorFlow, and cloud platforms like GCP or AWS. You might be asked to solve a technical problem during the interview, so practice coding challenges related to ML Ops and model deployment.
✨Communicate Clearly and Confidently
Excellent communication is key for this role. Practice explaining complex data science concepts in simple terms, as you'll need to convey your ideas to stakeholders who may not have a technical background. Confidence in your delivery can make a big difference!
