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

Nordstrom • Seattle, WA

1d • Hybrid

About The Position

Nordstrom is a specialty retailer offering the very best in fashion and customer service since 1901. We live by five simple values that guide how we work together day-to-day and how we deliver analytics & data science products. We are customer-obsessed, owners at heart, curious and ever-changing, we extend ourselves to our peers and our customers, and we’re here to win! The Digital Data Science team focuses on building and supporting AI/ML-powered products that shape the Nordstrom digital customer experience, including personalization and recommendation systems, conversational search, fraud detection, and other customer-facing intelligent features. As an integral part of our digital data science team, the Data Scientist I works alongside senior data scientists to develop and evaluate machine learning models that drive these products. This Data Scientist should have foundational knowledge of modern deep learning architectures, solid analytical skills to support model performance assessment through A/B testing and post-launch analysis, and the ability to communicate data insights and findings clearly to partners and the business. 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. The individual should be highly curious about the business and possess the skills to unlock rich, nuanced insights from complicated data and communicate those insights in a way that drives positive business outcomes. We are committed to building teams that reflect the diversity of our customers and active inclusion is core to how Nordstrom wins. We’re an equal opportunity employer and encourage individuals from all backgrounds to apply. If the idea to make a difference in this vibrant intersection of fashion, data science, and technology excites you, join our world-class data science team!

Requirements

• 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.

Nice To Haves

• 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

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.

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, and more.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: No Education Listed