Data Analyst 3, Merchandising Customer Insights (hybrid, Seattle)

Nordstrom Nordstrom · Retail · Seattle, WA

Data Analyst 3 role focused on merchandising customer insights for a fashion retailer. Responsibilities include analyzing complex data, developing measurement frameworks, hypothesis testing, delivering analytical products (analyses, dashboards, insights, recommendations), and presenting results to stakeholders. Requires SQL, R/Python, and experience with statistical/machine learning algorithms.

What you'd actually do

  1. Partner with key stakeholders to define success and develop measurement frameworks
  2. Dive deep into complex business problems and provide insights on merch performance through hypothesis testing
  3. Responsible for delivering a suite of analytical products include analyses, dashboards, insights, and recommendations.
  4. Present the results to the stakeholders up to executive level and guide them to make the best use of analytics in their domain.
  5. Own the definition and implementation of new metrics.

Skills

Required

  • SQL
  • R
  • Python
  • statistical algorithms
  • machine learning algorithms
  • regression
  • classification
  • clustering
  • data visualization
  • storytelling
  • interpersonal skills

Nice to have

  • BigQuery
  • Shiny
  • ggplot
  • matplotlib
  • Tableau

What the JD emphasized

  • 5+ years of professional experience analyzing complex data, drawing conclusions, and making recommendations.
  • 3+ years of experience in extracting & manipulating large data sets from various relational databases using SQL (BigQuery preferred).
  • Strong problem solving skills - Is able to complete ambiguous projects independently & plan out the priority order of tasks; find the right resources to complete the tasks and make smart decisions regarding trade-offs.
  • Strong coding skills in at least one statistical or programming language (R or Python preferred) to import, summarize, and analyze data.
  • Experience with statistical and machine learning algorithms (e.g. regression, classification and/or clustering). Proficiency with graphics/visualization software (e.g. Shiny, ggplot, matplotlib, Tableau).