Ralph Lauren Senior Data Scientist

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. Ralph Lauren's - India Technology group is focused on building & leveraging high-quality technology solutions to enhance business & digitization opportunities across channels and geographies

Position Overview
Purpose & Scope: The Senior Data Scientist role is a hands-on technical IC (Individual Contributor) role within the Data Science & AI team, driving the design, development, and deployment of scalable machine learning and AI solutions for complex business challenges. This role will require execution of E2E data science & AI initiatives, owning the delivery roadmap and collaboration with cross-functional stakeholders to drive data-driven transformation across Ralph Lauren.

Essential Duties & Responsibilities
What you will be doing (responsibilities):

• Own E2E Data Science/AI solutions including building out timelines/key milestones, prioritization, providing regular updates to product managers, analytics management and delivery against agreed timelines.
• Lead the identification of machine learning opportunities and design end-to-end solutions.
• Develop sophisticated machine learning models, including deep learning, recommendation systems, and natural language processing.
• Guide the deployment and monitoring of models in production environments, ensuring robustness, scalability, and performance.
• Design experiments, perform hypothesis testing, and evaluate model impact on business KPIs.
• Collaborate with business, engineering, and product teams to integrate AI/ML solutions into business workflows.
• Contribute to the data science & AI roadmap, advocating for AI/ML best practices and data-driven culture.

Experience, Skills & Knowledge
What you bring (Qualifications): Education & Experience:

• Master's degree in data science, computer science, applied mathematics, statistics, or a related quantitative field.
• 6-9 years of professional experience in advanced analytics and machine learning roles.
• Experience in analyzing complex and ambiguous business problems and translating them into data science/AI solutions.
• Strong expertise in Python, SQL, and experience with big data processing tools (Spark, Hadoop). Hands-on experience with deep learning frameworks (TensorFlow, PyTorch, Keras).
• Experience in machine learning, supervised and unsupervised learning: Natural Language Processing (NLP), classification algorithms, advanced data/text mining techniques, multi-modal model development, and deep neural network architecture.
• Experience in statistical learning methodologies including but not limited to Predictive & Prescriptive Analytics, Web Analytics, Parametric and Non-parametric models, Regression, Time Series Forecasting, Market Basket Analysis, Dynamic/Causal Inference Modeling, Statistical Learning, Guided Decisions, Topic Modeling
• Familiarity with Autonomous AI systems design, implementation of human-in-the-loop frameworks, and expertise in AI system monitoring, observability and governance practices.
• Experience in end-to-end LLM implementation including custom embedding generation, vector database management, LLM gateway configuration, RAG-based agent development, strategic model selection, iterative prompt refinement, model fine-tuning for accuracy optimization, and implementation of robust monitoring and governance frameworks. Proficiency in modern orchestration frameworks such as LangChain or DSPy.
• Experience in MLOps best practices, including production-grade model deployment at scale, and designing/implementing enterprise-level AI/ML governance structures.
• Expert communication and collaboration skills with the ability to work effectively with internal teams in a cross cultural and cross-functional environment. Ability to communicate conclusions to both tech and non-tech audiences Desired Skills:
• Retail industry experience and an understanding of retail/ecommerce data landscapes are strong pluses.
• Expertise in model interpretability techniques and ethical/responsible AI considerations.
• Familiarity with DevOps/DataOps practices for CI/CD of machine learning pipelines.
• Familiarity with Graph DB architecture.
• Proficiency in cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes).