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About This Role
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.
$191,000.00 - $297,000.00 Annual 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
This position may be eligible for performance-based incentives/bonuses. Benefits include 401k, medical/vision/dental/life/disability insurance options, PTO accruals, Holidays, and more. Eligibility requirements may apply based on location, job level, classification, and length of employment. Learn more in the Nordstrom Benefits Overview by copying and pasting the following URL into your browser: https://careers.nordstrom.com/pdfs/Ben_Overview_17-19.pdf 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.
Applicants with disabilities who require assistance or accommodation should contact the nearest Nordstrom location, which can be identified at www.nordstrom.com.
Please be mindful that there may be legal notices and requirements related to this job posting that are specific to your state. Review the Career Site FAQ’s for relevant information and guidelines.
Current Nordstrom employees: To apply, log into Workday, click the Careers button and then click Find Jobs.
Nordstrom keeps job postings open for at least one day after the posting date.
© 2026 Nordstrom, Inc
Salary Context
This $191K-$297K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 2239 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Nordstrom, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($244K) sits 15% above the category median. Disclosed range: $191K to $297K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Nordstrom AI Hiring
Nordstrom has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Seattle, WA, US, Seattle, WA. Compensation range: $297K - $297K.
Location Context
AI roles in Seattle pay a median of $226,100 across 1,095 tracked positions. That's 6% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (140) are outnumbered by mid-level (5,630) and senior (4,176) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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