Job Details
Company Description
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.
Position Overview
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.
Experience, Skills & Knowledge
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
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