Data Scientist - 2, Nordstrom Media Network, (Hybrid, WA)

About the Role

Key Responsibilities

• Develop and implement analytics applications to extract meaningful insights from large, disparate data sources using Python, R, SQL, and data visualization tools.
• Build and maintain onsite display and sponsored ads search relevancy models for optimized media performance.
• Build and enhance customized sales attribution models to connect marketing activities to short-term and long-term business outcomes.
• Optimize marketing investments by leveraging data-driven insights from modeling, forecasting, simulations, and testing.
• Design and analyze controlled experiments and A/B tests to validate model performance.
• Build and deploy robust data models that facilitate scalable statistical modeling and deep dive analyses for marketing use cases.
• Collaborate with partner teams to create data science products and solutions for stakeholders, translating questions into robust answers.
• Perform large-scale statistical analyses and develop regression models for sales attribution and incrementality.
• Work within and across teams to develop and deploy data products and data-driven software for analytical insights.
• Support consulting analytics teams with ad-hoc analyses.
• Lead code and documentation reviews, promoting best practices in Data Science and Machine Learning.

Requirements

• Bachelor's degree in mathematics, statistics, computer science, economics, marketing, analytics or in a quantitative field (or equivalent experience).
• 5+ years of hands-on professional experience in Data Science and Analytics.
• 3-5+ years of strong coding skills in at least one statistical or programming language (e.g., R, Python).
• Experience with onsite search personalization and advertisement relevancy.
• Experience with data visualization tools such as Tableau, Shiny, Streamlit, Looker, or Looker Studio.
• Expertise in statistical modeling, experimental design, inferential statistics, and machine learning, especially causal measurement and attribution models.
• Familiarity with marketing modeling techniques including transformations for diminishing returns, media synergy effects, and full funnel measurement.
• Proficiency with statistical and machine learning algorithms (e.g., regression, decision trees, clustering, survival analysis).
• Proficiency in extracting large data sets from relational databases using SQL (e.g., Redshift, Oracle, Teradata).
• Experience with retail marketing data such as Meta Ads and Google Ads.

Nice to Have

• Experience with onsite search personalization and advertisement relevancy.
• Experience with brand-specific retail marketing data (e.g., Meta Ads, Google Ads).

Qualifications

• Educational background in a quantitative field or equivalent experience.

Benefits & Perks

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