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.

Position Overview
The Senior Data Scientist is part of Ralph Lauren's Global Data and Analytics team and will work with line of business users, business analysts and architects to deliver insights by building and implementing various algorithms and machine learning models. The role will be responsible for maximizing value from data utilizing statistical software while working on high priority business opportunities across all functions and geographies. The Senior Data Scientist will collaborate with business users, business analysts, and data analysts to deliver insights by implementing algorithms and Machine Learning models using statistical performance metrics. Using regression, cluster analysis and time series models to explore relationships and trends in response to stakeholder questions and business challenges. Analyze internal and external datasets using analytical techniques, statistical performance metrics and visualization methods. Ensure pre-processing/cleansing of data, evaluate data points across enterprise landscape and/or external data points that can be leveraged in machine learning models to generate insights. Identify, test and train machine learning models using statistical performance techniques that need to be leveraged for business use cases. Evaluate models based on interpretability, performance and accuracy as required. Experiment and identify features from datasets that will help influence model outputs. Determine what statistical models will need to be deployed, data points that need to be fed into models and aid in deployment and maintenance of models. Communicate outputs of predictive statistical model via insights in a way that invites understanding and compels action across multiple levels of the organization. Develop partnerships with Business SMEs and Data Analysts: Utilize expertise on Ralph Lauren's data as well as industry best practices and drive innovation and adoption with the business community on advanced analytics. Collaborate with Data Analysts and Business SMEs to understand business problems, articulate how algorithms and Machine Learning models can be leveraged, build models that generate insights and aid in adoption with business partners. Propose statistical and analytical techniques and educate counterparts such as data analysts and business analysts in analytical techniques, which make it easier for them to integrate and consume the data they need for their own use cases. Telecommuting permitted; must report into NJ office as needed. Salary range $105,000-$155,000.

Experience, Skills & Knowledge
REQUIREMENTS: Bachelor's degree or equivalent in Computer Science/Information Systems, Statistics/Applied Mathematics, Data Science, or a related field and 5 years of experience in the position offered or related, or a Master's degree in the above mentioned fields and 2 years of experience in the position offered or related. Requires 2 years of experience with: data science and ML development life cycle and the tools and platforms around Data science and Machine Learning space; analytical or business intelligence disciplines including data analysis, visualization, feature engineering, and ML Modelling; statistical and analytical methods including regression, correlation analysis, time series forecasting and AI/ML techniques using deep learning, NLP, cluster analysis and classification models; working with advanced analytics tools for object-oriented/object function scripting using statistical software and languages such as R or Python; visualizations and Insights generation using MicroStrategy, Tableau, Qlikview or PowerBI; data management principles and Cloud Data engineering technologies like AWS tools (Redshift, S3, Athena), Azure, GCP, Teradata or Hadoop; and agile methodologies and applying DevOps and DataOps principles to data pipelines to improve the communication, integration, reuse and automation of data flows between data managers and consumers across an organization.