Senior Manager, Data Science

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 e:
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…