Job Description

The Opportunity

We are looking for a Senior Manager of Data Science to lead and evolve a team of data scientists and ML operations engineers responsible for improving how customers discover and shop for products across our digital experiences.

The team works across personalization, recommendations, search and browse relevance, customer and product understanding, experimentation, and measurement.

This leader will help evolve our Data Science organization from primarily building and evaluating models toward a broader mission: understanding customer problems, identifying the right interventions, measuring what actually creates value, and turning successful approaches into reusable capabilities that improve the shopping experience at scale.

Note: This position is offered as a hybrid role in the Seattle, WA area. You must be willing to work in office at the Nordstrom Seattle office for a minimum of 4 days per week to be considered for this position.

What You Will Lead

• Set the data science strategy for personalization, recommendations, search, browse, and digital product discovery.

• Partner with Product, Engineering, UX, and business leaders to identify the highest-value customer problems before deciding on a technical solution.

• Establish clear hypotheses, success measures, targets, guardrails, and decision criteria before significant experimentation begins.

• Move the team beyond asking only “Did model B outperform model A?” toward understanding why customer behavior changed, who benefited, and whether the change created meaningful customer and business value.

• Build a strong experimentation and measurement discipline that connects model performance to customer behavior, conversion, revenue, long-term engagement, and other meaningful outcomes.

• Develop the team's capabilities in experimentation, causal reasoning, optimization, personalization, recommendation systems, ranking, and customer understanding.

• Identify repetitive data science work that should be standardized, automated, or turned into reusable capabilities.

• Use GenAI and emerging AI capabilities to increase the speed of research, experimentation, evaluation, and learning, not simply to generate code faster.

• Champion AI-native ways of working across the team, including agentic architectures and LLMs in decisioning where they add value.

• Partner with Engineering to create scalable systems and platforms that allow successful scientific approaches to be reused broadly.

• Build, coach, and develop a high-performing team of data scientists and ML operations engineers, raising expectations for scientific rigor, customer understanding, business judgment, and technical excellence.

• Create an environment where scientists are expected to challenge assumptions, frame problems clearly, and recommend actions rather than simply report results.
  

What Success Looks Like

Within the first 12 months, this leader will have:

• Established a clear operating model for how Data Science frames problems, defines success, runs experiments, and makes recommendations.

• Increased the amount of team capacity spent on customer understanding, problem definition, experimentation strategy, and decision-making.

• Reduced repetitive manual work through standardized tools, workflows, and reusable capabilities.

• Improved the connection between offline model metrics, online experiments, customer outcomes, and business results.

• Developed a clear roadmap for how GenAI changes both the team's workflow and the capabilities it provides to customers and internal partners.

• Strengthened the team's technical and leadership talent to support the next stage of growth.
  

What We Are Looking For

• 10+ years of hands-on experience in Data Science or Machine Learning roles.

• 3+ years managing Data Science, Applied Science, or Machine Learning teams working on customer-facing products.

• Bachelor's degree in a quantitative field (e.g., statistics, computer science, mathematics, engineering) or equivalent practical experience; advanced degree preferred.

• Deep experience in at least one of the following: personalization, recommendation systems, search/ranking, customer modeling, marketplace optimization, or digital commerce.

• Strong knowledge of experimentation, statistical measurement, and causal inference.

• Demonstrated ability to connect scientific work to product and business decisions.

• Experience moving teams from bespoke model development toward reusable platforms, frameworks, or scalable capabilities.

• Experience applying modern machine learning and GenAI techniques to real customer or business problems, including daily use of AI tools (e.g., Claude Code) and agentic or LLM-based approaches in decisioning.

• Hands-on comfort with Python and SQL and working with large-scale data (e.g., BigQuery), enough to read, review, and guide the team's code and outputs. Familiarity with open-source AI resources (e.g., Hugging Face, OpenRouter) and cloud ML platforms (Google Vertex AI, AWS) is a plus.

• Strong product and business judgment; able to distinguish technically interesting work from work that materially improves customer outcomes.

• Proven ability to lead organizational change, establish new standards, and raise the performance bar of an existing team.

• Strong people leadership, including hiring, coaching, performance management, succession development, and creating strong technical leadership within the organization.

• Ability to communicate complex scientific ideas in clear language and influence senior Product, Engineering, and business leaders.

The Mindset We Value

You believe the job of Data Science is not simply to build models. This is an applied data science role, not a research or reporting function: you identify the problem, propose a solution, iterate quickly, and deliver with Engineering.

You start with the customer and the decision that needs to be made. You use science to reduce uncertainty, test assumptions, understand what actually works, and help the organization make better decisions.

You see AI as an opportunity to eliminate repetitive work, increase the speed of learning, and expand what a small team can accomplish.

And you measure the success of a Data Science organization by the quality of the decisions and customer outcomes it enables, not by the number of models it produces.

Pay Range Details

The pay range(s) below has been provided in compliance with state specific laws. Pay ranges may be different for other locations. 
Pay offers are dependent on the location, as well as job-related knowledge, skills, and experience.

We’ve got you covered…

Our employees are our most important asset and that’s reflected in our benefits. Nordstrom is proud to offer a variety of benefits to support employees and their families, including:

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

A few more important points...

The job posting highlights the most critical responsibilities and requirements of the job. It’s not all-inclusive. There may be additional duties, responsibilities and qualifications for this job.

For Los Angeles or San Francisco applicants: Nordstrom is required to inform you that we conduct background checks after conditional offer and consider qualified applicants with criminal histories in a manner consistent with legal requirements per Los Angeles, Cal. Muni. Code 189.04 and the San Francisco Fair Chance Ordinance. For additional state and location specific notices, please refer to the Legal Notices document within the FAQ section of the Nordstrom Careers site.

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