Senior Manager, Data Science

Nordstrom•Seattle, WA

5d•$191,000 - $297,000•Hybrid

About The Position

Nordstrom is seeking a Senior Manager of Data Science to lead and develop a team of data scientists and ML operations engineers working to improve how customers discover and shop for products across digital experiences. The team works on personalization, recommendations, search and browse relevance, customer and product understanding, experimentation, and measurement. The leader will evolve the organization beyond primarily building and evaluating models. The role focuses on understanding customer problems, identifying appropriate interventions, measuring value, and turning successful approaches into reusable capabilities that improve the shopping experience at scale. The position is hybrid in the Seattle, Washington area and requires working from Nordstrom’s Seattle office at least four days per week. The Senior Manager will set data science strategy; partner with Product, Engineering, UX, and business leaders; establish rigorous experimentation and measurement practices; and connect model performance to customer behavior and business outcomes. The role also develops team capabilities in experimentation, causal reasoning, optimization, personalization, recommendations, ranking, and customer understanding; applies GenAI and emerging AI capabilities to speed research and learning; and partners with Engineering on scalable systems and platforms. The leader will build, coach, and develop a high-performing team, establish an operating model for problem framing and decision-making, reduce repetitive manual work through reusable tools and workflows, and strengthen the organization’s technical and leadership capabilities. Success includes improving the relationship between offline model metrics, online experiments, customer outcomes, and business results, as well as developing a roadmap for GenAI’s impact on team workflows and customer and partner capabilities. This is an applied data science leadership role, not a research or reporting function. The leader is expected to start with customer problems and decisions, use science to reduce uncertainty and test assumptions, work with Engineering to deliver solutions, and measure success through better decisions and customer outcomes.

Requirements

At least 10 years of hands-on experience in Data Science or Machine Learning roles.
At least 3 years managing Data Science, Applied Science, or Machine Learning teams working on customer-facing products.
Bachelor's degree in a quantitative field such as statistics, computer science, mathematics, or engineering, or equivalent practical experience.
Deep experience in at least one of 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 and agentic or LLM-based approaches in decisioning.
Comfort working hands-on with Python, SQL, and large-scale data, sufficient to review and guide the team's code and outputs.
Strong product and business judgment, including the ability 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 developing technical leadership.
Ability to communicate complex scientific ideas clearly and influence senior Product, Engineering, and business leaders.
Willingness to work in Nordstrom's Seattle office at least four days per week.

Nice To Haves

Advanced degree preferred.
Familiarity with open-source AI resources such as Hugging Face and OpenRouter.
Familiarity with cloud ML platforms such as Google Vertex AI and AWS.

Responsibilities

Set data science strategy for personalization, recommendations, search, browse, and digital product discovery.
Partner with Product, Engineering, UX, and business leaders to identify high-value customer problems before selecting technical solutions.
Define hypotheses, success measures, targets, guardrails, and decision criteria before major experimentation.
Build experimentation and measurement practices that connect model performance to customer behavior, conversion, revenue, long-term engagement, and other outcomes.
Develop team capabilities in experimentation, causal reasoning, optimization, personalization, recommendation systems, ranking, and customer understanding.
Identify repetitive data science work to standardize, automate, or turn into reusable capabilities.
Use GenAI and emerging AI capabilities to accelerate research, experimentation, evaluation, and learning.
Champion AI-native ways of working, including agentic architectures and LLMs in decisioning where valuable.
Partner with Engineering to develop scalable systems and platforms that enable reuse of successful scientific approaches.
Build, coach, and develop a high-performing team of data scientists and ML operations engineers.
Raise expectations for scientific rigor, customer understanding, business judgment, and technical excellence.
Create an environment where scientists challenge assumptions, clearly frame problems, and recommend actions.
Establish an operating model for problem framing, defining success, experimentation, and recommendations.
Increase team capacity devoted to customer understanding, problem definition, experimentation strategy, and decision-making.
Reduce repetitive manual work through standardized tools, workflows, and reusable capabilities.
Improve the connection between offline model metrics, online experiments, customer outcomes, and business results.
Develop a roadmap for how GenAI changes team workflows and capabilities offered to customers and internal partners.
Strengthen the team's technical and leadership talent.
Lead hiring, coaching, performance management, and succession development, and foster technical leadership.
Communicate scientific ideas clearly and influence senior Product, Engineering, and business leaders.