Data Analyst 2, Stores & Finance (Hybrid, Seattle)

Nordstrom Inc.•Seattle, WA

32d•Hybrid

About The Position

The vision for Data Science & Analytics at Nordstrom is to be the best data-driven fashion retailer in the world, and we win by being relevant to the customer through an application of insight and prediction to outcomes.â¯The Customer Insights & Analytics team supports this vision by empowering the executive team with robust data products and analytical consulting that helps steer short- and long-term strategic decisions We are looking for a creative, curious, and passionate Data Analyst 2 to join our Stores Analytics team. The core function of this role will include partnering with our new store openings and Real Estate teams to identify the ideal locations for our new store locations, optimize overall store experience and leverage digital signals to provide an omni-channel experience for our customers. This person will also work broadly with our Store partners across leadership, technology, strategy, and finance on high-impact projects. The ideal candidate is a creative self-starter and strong technical contributor who is always looking for new opportunities to solve business problems with data-driven tools. In this hands-on role, you will deliver end-to-end analyses that transform data into actionable insights that directly inform stores optimization and informing strategy that allows our customers to 'Feel Good and Look Their Best.' The role has an opportunity to tackle challenges from ideation to insights delivery, with visibility across the leadership organization. This individual should have a high degree of curiosity about the business and the skills to discover impactful insights from data and communicate those insights in a way that builds confidence and enables decisions that drive business value. This position will join a team of data scientists and analysts, reporting into the Sr. Manager of Data Science & Analytics, Store Analytics.

Requirements

Bachelor's degree in mathematics, statistics, computer science, economics, operations research or in a quantitative field (or equivalent experience).
2+ years hands-on professional experience in Data Science and Analytics.
2+ years of strong coding skills in at least one statistical or programming language (e.g., R, Python) to import, process, summarize, and analyze data.
Experience with utilizing data visualization tools to tell compelling data stories (e.g., Power BI, Tableau, Shiny, Plotly, GGPlot)
A solid grasp of the scientific method and its application to Data Science.
Fluency with descriptive and inferential statistical techniques, including experimental design (DOE) concepts and their application.
Proficiency with statistical techniques and when best to apply given current situation.
Proficient in extracting large data sets from various relational databases using SQL (Amazon Redshift, Oracle, GCP preferred).
Experience managing large projects or workstreams as a Data Analyst / Data Scientist.

Nice To Haves

Strong critical thinking and storytelling skills.
3+ years of experience analyzing complex data, drawing conclusions, and making recommendations.
3+ years of experience developing and deploying production-level SQL queries.
3+ years of experience with advanced BI visualization (Tableau or Looker preferred).
Ability to think strategically, seek to expand understanding of business domain, processes, data sources, and relevant key metrics.
MS or PhD in mathematics, statistics, computer science, economics, operations research or in a quantitative field (or equivalent experience).
Passion and aptitude for turning sophisticated problems into concrete hypotheses that can be answered through meticulous data analysis and statistical testing.
Experience working in a highly collaborative environment (e.g., code sharing, using revision control, contributing to team discussions/workshops, and document sharing).
Familiarity with e-commerce analytics topics (e.g., digital tactics, targeting and customer segmentation, lifetime value forecasting, and incremental response modeling, to name a few).

Responsibilities

Use storytelling to present data in a clear and engaging manner, making it accessible and meaningful to audiences with varying degrees of technical expertise.
Provide high-quality data visualizations to support analyses, presented through automated dashboards and ad-hoc reports.
Manage analyses across competing priorities to focus on the most impactful work to maximize the value of your work to the business.
Partner with key stakeholders to define success and develop measurement frameworks.
Present the results to the stakeholders and guide them to make the best use of the insights for their purposes and use-cases.
Use advanced analytical and statistical practices to create thorough and actionable insights.