Data Scientist 2 - 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 is responsible for developing, deploying, and evaluating machine learning models that drive these products. This Data Scientist should have deep expertise in modern deep learning architectures, strong analytical skills to assess model performance through A/B testing and post-launch analysis, and the ability to communicate data insights and findings to Nordstrom Leadership 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

5+ years hands-on professional experience in Data Science and Analytics, with 3+ years in a Data Scientist role.
3+ years of strong coding experience with at least one statistical or programming language (e.g., R, Python) to import, process, summarize, and analyze data.
Experience working in a highly collaborative technical environment (e.g., code sharing, using revision control, contributing to team discussions/workshops, and document sharing).
3+ years of corporate experience with machine learning and deep learning algorithms, including strong working knowledge of transformer architectures and modern neural networks (e.g., attention mechanisms, embeddings, fine-tuning large pre-trained models, as well as classical methods such as gradient boosting, collaborative filtering, etc.).
3+ years of corporate experience extracting large data sets from various relational and non-relational databases using SQL and big-data tools such as Hive or Spark.
Hands-on experience deploying ML models into production environments and conducting A/B testing to evaluate model performance and business impact.
Passion and aptitude for turning complex business problems into concrete hypotheses that can be answered through rigorous data analysis and experimentation.
Demonstrated expertise in analytical storytelling and communication of insights to business partners and leadership.

Nice To Haves

Experience in leading data science project with more than 1 people
Experience with projects including building, deploying, and maintaining ML models in real-time production environments serving customer-facing products (example tools: GCP vertex, Sagemaker, Kubernetes, TensorFlow Serving, TorchServe).
Experience developing and deploying automated data pipelines using cloud services (e.g. 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.
Demonstrated success in mentoring data scientists and analysts to help them grow in both technical skills and business acumen.
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 GCP tools and other cloud services.
Understand basics of end-to-end model deployment, from prototyping and training through production serving, monitoring, and iteration, ensuring models perform reliably at scale.
Work within and across teams to develop and deploy data products and data-driven software, driving collaboration and adoption on major data-science initiatives.
Design and conduct rigorous A/B testing and post-launch analysis to measure model impact, diagnose why models are or are not working, and drive 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.
Be a mentor and technical SME for other data scientists and analysts, contributing to team growth in terms of both technical skills and business acumen.