Data Analyst 2 - Store Loyalty, Customer Insights and Analytics - Hybrid - Seattle

Nordstrom

Nordstrom is redefining the retail sector with the use of data and AI. The Data Science & Analytics team aims to be a leading force in the fashion retail space by supporting executive decisions with comprehensive data science. This position will enhance customer insights and loyalty, helping shape the store's strategic business decisions.

In this role you can expect to have the responsibilities:

• Conduct diverse and complex ad hoc analyses to derive key business insights.
• Develop and maintain dashboards in Tableau for store performance and KPI monitoring.
• Design and evaluate key performance indicators and metrics.
• Apply experimental design for insightful call center incentives analytics.
• Lead the A/B testing process and set analytical priorities.
• Collaborate with Card Services departments on operational initiatives and expectations.
• Prepare various reports and PowerPoint presentations for management.

This role comes with the following benefits:

• Medical/Vision, Dental, Retirement and Paid Time Away
• Life Insurance and Disability
• Merchandise Discount and EAP Resources

This role requires you to have:

• 3+ years of experience in Data Science and Analytics.
• Proficiency in languages like SQL, R, or Python for data processing.
• Expertise in data visualization tools such as Tableau or PowerBI.
• Bachelor's degree in quantitative fields or equivalent experience.
• Knowledge of statistical techniques and their applications.

You would benefit from having:

• Critical thinking and data storytelling skills.
• Experience in BI visualization tools like Tableau or Looker.
• MS or PhD in relevant quantitative fields.
• Collaborative skills including code sharing and team workshops.

Nordstrom adheres to legal requirements regarding applicants with criminal histories and provides reasonable accommodation for applicants with disabilities. This posting highlights critical job responsibilities and is not all-inclusive.