Lead Data Analyst- Merchandising Analytics Essentials and Beauty

Target Target · Retail · Minneapolis, MN

This role is for a Lead Data Analyst focused on Merchandising Analytics, specifically within the Essentials and Beauty categories at Target. The primary responsibilities involve creating tools and data products to enable data-based decision-making, focusing on data sourcing, visualizations, and providing actionable insights. The role supports strategic merchandising initiatives with data, reporting, and analysis, and plays a key part in identifying A/B testing opportunities. Key tasks include exploring data, deriving business insights, gathering, modeling, analyzing, and presenting data, creating ad hoc analyses and dashboards, and developing new data and metrics for a new initiative. The role requires strong SQL, Python/R skills, statistical analysis knowledge, and experience with various analytics tools. It is a hybrid role based in Minneapolis, MN.

What you'd actually do

  1. connect teams with trusted data and high-quality insights
  2. deliver world-class product solutions in partnership with our product teams
  3. driving prioritized metrics and efficient insights
  4. embrace a continuous-learning mindset

Skills

Required

  • Four-year degree or equivalent experience
  • 6+ year as a Data Analyst with strong academic performance in a quantitative field; or strong equivalent experience
  • Advanced SQL experience writing complex queries
  • Accomplished with Python or R
  • Solid problem solving, analytical skills, data curiosity, data mining, Data creation and consolidation
  • Support conclusions with a clear, understandable story that leverages descriptive statistics, basic inferential statistics, and data visualizations
  • Willingness to ask questions about business objectives and the measurement needs for a project workstream, and be able to measure objectives & key results
  • Excellent communication skills with the ability to speak to both business and technical teams, and translate ideas between them
  • Knowledge of AB Testing methods, time series, S&OP planning, Forecasting models including statistical analysis
  • Experience in analytics tools such as: SQL, Excel, Hadoop, Hive, Spark, Python, R, Domo, Adobe Analytics (or Google Analytics) and/or equivalent technologies

What the JD emphasized

  • trusted data
  • product solutions
  • prioritized metrics
  • continuous-learning
  • Merchandising Analytics
  • data sourcing
  • data visualizations
  • action oriented insights
  • test-and-measure or A/B test opportunities
  • data, reporting and analysis
  • derive business insights
  • gather, model, manipulate, analyze and present data
  • Ad hoc analysis
  • dashboard creation
  • providing insights that drive decisions
  • new data and metric development
  • guest level insights