Data Scientist - Digital Data Science - Hybrid - Seattle, WA

Nordstrom is a specialty retailer offering the very best in fashion and customer service since 1901. They are seeking a Data Scientist I to join their Digital Data Science team, focusing on building AI/ML-powered products that enhance the digital customer experience. The role involves collaborating with cross-functional teams to develop machine learning models and drive data-informed product features.

Responsibilities

• Collaborate with product, engineering, and cross-functional partner teams to build and improve ML-powered digital products such as personalization, recommendation, conversational search, and fraud detection systems
• Design, train, fine-tune, and evaluate deep learning models, including transformer-based architectures, for applications such as recommendation, ranking, natural language understanding, and anomaly detection
• Extract and prepare large sets of data for analysis; improve existing data resources by building data pipelines using AWS/GCP tools and other cloud services
• Support senior data scientists on model deployment, contributing to prototyping, training, testing, and monitoring, while learning production serving best practices
• Work within and across teams to develop and deploy data products and data-driven software, driving collaboration and adoption on major data-science initiatives
• Support A/B testing and post-launch analysis to measure model impact, help diagnose why models are or are not working, and contribute to data-informed iteration on product features
• Help develop and drive adoption of best practices in all aspects of the Data Science workflow, including intake, design, code review, testing, automation, documentation, reporting, and long-term maintainability
• Actively learn from senior data scientists and analysts, growing in both technical skills and business acumen while contributing fresh perspectives to the team

Skills

• 1–3 years of hands-on professional experience in Data Science, Analytics, or a related quantitative field (including internships or research positions)
• 1+ years of working experience with Python or R, including data manipulation, analysis, and visualization
• Experience working in a highly collaborative technical environment (e.g., code sharing, using revision control, contributing to team discussions/workshops, and document sharing)
• 1+ years of working experience with machine learning and deep learning algorithms, including familiarity with transformer architectures and modern neural networks (e.g., attention mechanisms, embeddings, fine-tuning pre-trained models, as well as classical methods such as gradient boosting, collaborative filtering, etc.)
• 1+ years of working experience extracting and manipulating data using SQL; exposure to big-data tools such as Hive or Spark is a plus
• Exposure to ML model deployment workflows and A/B testing concepts; eagerness to learn production ML practices
• Passion and aptitude for turning complex business problems into concrete hypotheses that can be answered through rigorous data analysis and experimentation
• Ability to communicate analytical findings clearly to technical and non-technical audiences
• Experience contributing to data science projects in a team setting (academic, internship, or professional)
• Experience developing packages in Python along with clear documentation
• Exposure to ML model serving or deployment tools (e.g., SageMaker, AzureML, Docker, Flask) through coursework, personal projects, or internships
• Experience developing and deploying automated data pipelines using cloud services (e.g. AWS/GCP)
• Strong background in model explainability, interpretability, and diagnostics—ability to systematically analyze and articulate why a model is or is not performing as expected
• Experience with causal inference and experimentation frameworks, including designing and analyzing online experiments to measure incremental model impact
• Familiarity with digital product domains such as personalization and recommendation engines, conversational/semantic search, fraud detection, or similar real-time, customer-facing ML applications
• Eagerness to learn from experienced data scientists and a proactive attitude toward professional growth
• Experience with NLP, LLM integration, or retrieval-augmented generation (RAG) pipelines for search or conversational AI applications is a plus

Benefits

• Medical/Vision, Dental, Retirement and Paid Time Away
• Life Insurance and Disability
• Merchandise Discount and EAP Resources
• 401k
• Medical/vision/dental/life/disability insurance options
• PTO accruals
• Holidays

Company Overview

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