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Ralph Lauren

Data Scientist

Ralph Lauren
United Kingdom · Full-time · Associate

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.

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We are looking for a Data Scientist at the early stages of their career to join our Integrated Business Planning (IBP) Advance Analytics team. This role is ideal for individuals who are passionate about using data to solve real-world problems, uncover insights, and build scalable models to support business decision-making.

You will work closely with cross-functional teams including IT, merchandising, supply chain and external partners and receive guidance while progressively building your understanding of our data, processes, and business context.

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  • Prepare datasets for analysis by cleaning, transforming, and validating data using standard techniques.
  • Apply statistical methodologies to conduct empirical analysis and identify data anomalies
  • Design and implement statistical and machine learning solutions for business challenges. (e.g. forecasting, elasticity modeling, and clustering)
  • Apply broader domain knowledge to evaluate and interpret results with greater autonomy.
  • Shape the design and monitoring of experiments and interpret their outcomes with statistical rigor.
  • Drive the development and deployment of scalable model solutions.
  • Develop clear, impactful visualizations and reports to convey findings to stakeholders
  • Collaborate with stakeholders to gather requirements and understand business objectives.
  • Perform a variety of analytical tasks across different projects, adapting to changing priorities.
  • Identify and implement improvements to existing data workflows or analytical approaches.

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  • Demonstrated knowledge and expertise equivalent to advanced education in Data Science, Computer Science, Statistics, Mathematics, or related fields.
  • Proven experience in data science, advanced analytics, or quantitative analysis
  • Strong programming skills in Python or R, with experience in data analysis libraries (pandas, scikit-learn)
  • SQL proficiency for data querying and manipulation.
  • Proficiency with data visualization tools (e.g., Microstrategy, Tableau) or dashboarding in Python (e.g., Plotly, Streamlit).
  • Solid foundation in machine learning concepts, statistical methods, and data preparation techniques
  • Comprehensive understanding of experimentation methods, model evaluation, and business metric interpretation.
  • Effective written and verbal communication skills, especially when explaining technical topics to non-technical audiences.
  • Strong collaborative mindset and receptiveness to feedback
  • Demonstrated ability to handle multiple assignments or projects.
  • Familiarity with version control tools (e.g., Git) and Agile work practices.
  • Exposure to cloud platforms (e.g., AWS, GCP, Azure) or big data tools (e.g., Spark) is a plus.
  • Retail/apparel industry knowledge is advantageous

Key Skills

Ranked by relevance

machine learning python data visualization data analysis streamlit big data tableau pandas cloud spark git aws gcp
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Posted
Aug 05, 2025
Type
Full-time
Level
Associate
Location
London

Industries

Retail Apparel Fashion

Categories

Engineering Information Technology

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