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
Purpose & Scope: This remote Data Scientist position will work as part of an elite team alongside data engineers and data analysts focused on maximizing value from data while working on high priority business opportunities across all functions and geographies. The Data scientist will collaborate with line of business users, business analysts and Solution Architects to deliver insights by implementing various algorithms and ML models. This role will leverage analytical and data science skills to solve business problems, unlock opportunities and create new insights. They will identify and explore internal and external data sets. They will leverage predictive and machine learning models to unlock actionable insights with data and inspire data driven actions. This role will require both creative and collaborative working with IT and the wider business. It will involve evangelizing effective data management practices and promoting better understanding of data and analytics. The Data Scientist will also be tasked with working with key business stakeholders, IT experts and subject-matter experts to plan and deliver optimal analytics and data science solutions.

Essential Duties & Responsibilities
· Analyze Enterprise data to unlock insights: Move beyond descriptive reporting helping stakeholders identify relevant insights and actions from data. Use regression, cluster analysis, time series, etc. to explore relationships and trends in response to stakeholder questions and business challenges. · Bring in Thought Leadership for AI an ML: Bring in Retail Industry experience and apply the same to build efficient and optimal Machine Learning solutions. · Exploratory Data Analysis and Generate Insights: Analyze internal and external datasets using analytical techniques, tools 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. · DS and ML Model Identification and Training: Identify, test and train machine learning models 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. The Data Scientist will determine what models will need to deploy, data points that need to be fed into models and aid in deployment and maintenance of models. · 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. This role will collaborate with Data Analysts and Business SMEs to understand business problems, articulate how algorithms and ML models can be leveraged, build models that generate insights and aid in adoption with business partners. Educate and train: The Data Scientist should be knowledgeable about new data initiatives and how advanced analytics will help solve business problems. They will be required to guide and educate junior data scientists within the team, citizen data scientists, data analysts and business analysts in analytical techniques and make it easier for them to integrate and consume the data they need for their own use cases.

Experience, Skills & Knowledge
Education and Experience · Progressively responsible work experience in computer science, statistics, applied mathematics, data management, information systems, information science or a related quantitative field is required. · An advanced degree in computer science (MS), statistics, applied mathematics (Ph.D.), information science (MIS), data management, information systems, information science (postgraduation diploma or related) or a related quantitative field is preferred. · The ideal candidate will have a combination of analytical skills, data governance skills, IT skills and Retail industry knowledge with a technical or computer science degree. · At least 5 years or more of work experience in Data science and ML technologies. · At least 2 years or more of work experience working with business stakeholders in Retail / Apparel Industry in support of a departmental and/or multi-departmental analytics initiative. Technical Knowledge/Skills · Strong experience with machine learning and AI including regression, forecasting, time series, cluster analysis, classification, Image recognition, NLP, Text Analytics and Computer Vision. · Strong experience with advanced analytics tools for Object-oriented/object function scripting using languages such as R, Python, or similar. · Strong experience with popular database programming languages including SQL, PL/SQL, etc. for relational databases and on NoSQL/Hadoop oriented databases like MongoDB, Cassandra, etc for nonrelational databases. · Strong experience in working with data science teams in optimizing and implementing enterprise level data science and machine learning models and algorithms. · Strong experience with popular database querying languages including SQL, PL/SQL, etc. for relational databases like Redshift and on NoSQL/Hadoop oriented databases like MongoDB, Cassandra, etc for nonrelational databases and experience with AWS Data Lake and Cloud Data engineering technologies - S3, Glue, Athena, Redshift etc. · Relevant experience working with popular data discovery, analytics and BI software tools like MicroStrategy, Tableau, Qlik, PowerBI and others for semantic-layer-based data discovery. · Basic understanding of popular open-source and commercial data science platforms (Auto ML) such as KNIME, Alteryx, Dataiku or others is a strong plus. · Basic experience in working with data governance, data quality, and data security teams and specifically and privacy and security officers in moving data pipelines into production with appropriate data quality, governance and security standards and certification. · Demonstrated ability to work across multiple deployment environments including cloud, on-premises and hybrid, multiple operating systems and through containerization techniques such as Docker, Kubernetes, AWS Elastic Container Service and others. · Adept in agile methodologies and capable of applying DevOps and increasingly DataOps principles to data pipelines to improve the communication, integration, reuse and automation of data flows between data managers and consumers across an organization Interpersonal Skills and Characteristics · Strong experience supporting and working with cross-functional teams in a dynamic business environment. · Required to be highly creative and collaborative. An ideal candidate would be expected to collaborate with both the business and IT teams to define the business problem, refine the requirements, and design and develop data deliverables accordingly. The successful candidate will also be required to have regular discussions with data consumers on optimally refining the data pipelines developed in nonproduction environments and deploying them in production. · Required to have the accessibility and ability to interface with stakeholders at all levels and roles within the company. · Is a confident, energetic self-starter, with strong interpersonal skills. Has good judgment, a sense of urgency and has demonstrated commitment to high standards of ethics, regulatory compliance, customer service and business integrity.