Purpose & Scope:
This hybrid schedule Senior Data Analyst role 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 Senior Data Analyst will collaborate with line of business users, business analysts, Data Engineers and data scientists on models and algorithms to deliver analytics insights and use cases.
The Senior Data Analyst 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 Senior Data Analyst 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 Senior Data Analyst 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 Senior Data Analyst 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 Senior Data Analyst 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, Solution Architects, 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
- Progressively responsible work experience in the fields of computer science, statistics, applied mathematics, data management, information systems, information science or a related quantitative field
- 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 / Apparel Industry 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 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 and relevant experience in Retail industry is a must. Experience in Fashion and Apparel industry would be ideal.
- 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.
- Relevant experience in working with data science teams in refining and optimizing data science and machine learning models and algorithms in the Retail Industry.
- Relevant Experience with machine learning and AI including regression, forecasting, time series, cluster analysis, classification would be a plus.
- Relevant experience with advanced analytics tools for Object-oriented/object function scripting using languages such as R, Python, Scala, or similar.
- Relevant experience with AWS Data Lake and Cloud Data engineering technologies – S3, Glue, Athena, Redshift etc.
- Basic understanding of popular open-source and commercial data science platforms such as Python, R, KNIME, Alteryx, Dataiku 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.