Analytics Solutions Designer
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, and Club Monaco, among others, constitute one of the world's most widely recognized families of consumer brands.
Based in Nutley, NJ this Analytics Solution Designer will work as part of an elite team alongside data engineers and data visualization engineers focused on maximizing value from data while working on high priority business opportunities across all functions and geographies. The Analytics Solution Designer will collaborate with line of business users, business analysts, data analysts and data scientists on models and algorithms to deliver analytics insights and use cases.
The Analytics Solution Designer will leverage analytical, visualization, and data engineering skills to solve problems, unlock opportunities and create new insights. They will identify and explore internal and external data sets. They will use visualizations and storytelling with data to share insights 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 Analytics Solution Designer 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 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.
Create visualizations and tell great stories with data: The Analytics Solution Designer must be able to communicate insights in a way that invites understanding and compels action across multiple levels of the organization.
Develop strong partnerships with key line of business stakeholders: Utilize expertise on Ralph Lauren's data and industry best practices to develop strong partnership with key stakeholders across business units in order to expand the analytics capabilities of the organization. The Analytics Solution Designer will understand the needs of stakeholders as well as push the organization to adopt new ways of analyzing and visualizing data.
Design analytics solutions to the problems faced by stakeholders: Provide thought leadership to define creative solutions to problems that balance speed of execution with the ability to create sustainable wins. Understand the various sources of data, technical components of the architecture, and best practices in the industry to solve problems with speed. Ensure the design is well understood and embraced by team members.
Drive delivery of solutions in an Agile delivery model: Document requirements and solution design as stories that can be completed within a sprint. Work as part of the sprint team to ensure requirements and design are well understood and achieve the expected value.
Educate and train: The Analytics Solution Designer should be curious and knowledgeable about new data initiatives and how to address them. This includes applying their data and/or domain understanding in addressing new requirements. They will also be responsible for proposing appropriate (and innovative) data analysis and visualization techniques. They will be required to train counterparts such as data scientists, data analysts, LOB users or any data consumers in analysis and visualization techniques, which make it easier for them to integrate and consume the data they need for their own use cases.
Experience, Skills & Knowledge
Education and Experience
A bachelor's or master's degree 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 8 years or more of work experience in analytical or business intelligence disciplines including data analysis, visualization, integration, modeling, etc.
At least 3 years of experience working in cross-functional teams and collaborating with business stakeholders in Retail in support of a departmental and/or multi-departmental analytics initiative.
Deep Retail Industry knowledge or previous experience working in the business would be a plus.
Strong experience with analytical methods including regression, forecasting, time series, cluster analysis, classification, etc. Experience with machine learning and AI would be a plus.
Strong experience with advanced analytics tools for Object-oriented/object function scripting using languages such as R, Python, Scala, 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 working with popular data discovery, analytics and BI software tools like MicroStrategy, Tableau, Qlik, PowerBI and others for semantic-layer-based data discovery. Certification in one more of these tools would be a plus.
Strong experience in working with data science teams in refining and optimizing data science and machine learning models and algorithms.
Basic understanding of popular open-source and commercial data science platforms such as Python, R, KNIME, Alteryx, 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, and gain the respect of, 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.