Principal Product Manager - Inventory Intelligence (Hybrid - Seattle)

Nordstrom•Seattle, WA

1d•Hybrid

About The Position

Nordstrom's Inventory and Supply Chain team is seeking a highly technical product leader to drive the development of AI-powered optimization models and intelligent orchestration platforms that will transform how we manage inventory across our retail network. This role offers an exceptional opportunity to build mathematical optimization capabilities, agentic AI systems, and autonomous decision-making platforms that maximize inventory value while improving customer experience. As the Principal Product Manager for AI-Powered Inventory Intelligence, you will work hands-on with data science and engineering teams to build production-grade optimization models for dynamic inventory positioning, develop AI agents that coordinate across specialized systems, and create the technical foundation for autonomous inventory management. You will be responsible for translating complex optimization problems into mathematical models, validating model performance through rigorous testing and backtesting, and architecting scalable AI systems that make real-time decisions across millions of SKUs and hundreds of locations. This role is designed for a technical product leader with deep AI/ML expertise who can bridge advanced analytics and production engineering, collaborate directly with data scientists on model architecture, and drive transformational capabilities that represent significant business value for the enterprise. A day in the life… Develop mathematical optimization models for inventory positioning decisions that maximize expected value by evaluating selling probability, inventory projections, capacity constraints, price trajectories, transportation times, and operational costs across our network Work hands-on with data scientists on model architecture, feature engineering, algorithm selection, and validation methodologies for production AI systems Build and validate optimization algorithms for dynamic allocation, replenishment, rebalancing, and recovery positioning including cross-banner returns and clearance inventory optimization Conduct backtesting, measure model performance using precision/recall/accuracy metrics, and iterate on model design to achieve business outcomes including cycle time reduction, improved recovery value, and margin optimization Make technical architecture decisions on optimization frameworks, data contracts, model integration patterns, and how AI agents coordinate to make autonomous decisions Partner with engineering teams to define technical requirements for real-time data pipelines, model serving infrastructure, and integration with warehouse management systems, transportation systems, and inventory platforms Design and validate A/B testing strategies to measure model impact on business metrics including inventory turns, recovery rates, in-stock improvement, and financial outcomes Build orchestration capabilities that coordinate specialized AI models (inventory placement, demand forecasting, performance optimization) to make holistic, autonomous inventory decisions Define model input requirements including demand forecasts, inventory projections, cost structures, capacity constraints, and business rules, working with data engineering teams to ensure data quality and availability Collaborate with cross-functional stakeholders in Supply Chain, Merchandising, Finance, and Operations to translate business requirements into technical optimization problems and validate model outputs against operational constraints Develop technical product roadmaps for AI-enabled inventory capabilities, balancing quick wins with long-term platform vision Lead technical discussions with data science teams on model selection (regression vs. classification vs. optimization), feature importance, hyperparameter tuning, and production deployment strategies Build trust mechanisms and explainability frameworks for AI recommendations to enable business users to understand and adopt model-driven decisions Define and track technical KPIs for model performance (latency, accuracy, precision, recall, business metrics) and establish monitoring and alerting for production AI systems Drive technical discovery and feasibility assessments for new AI use cases, evaluating build vs. buy decisions and determining technical approach for complex optimization problems

Requirements

10+ years of product management experience with deep focus on AI/ML product development, mathematical optimization, or data science-driven products
Proven track record of building and shipping production AI/ML systems at scale, working hands-on with data scientists on model development, validation, and deployment
Strong technical background in mathematical optimization, operations research, machine learning, or related quantitative fields with ability to collaborate directly with data scientists on model architecture and algorithms
Deep understanding of model validation methodologies including backtesting, A/B testing, precision/recall/accuracy metrics, and production model monitoring
Experience with optimization problems in supply chain, inventory management, logistics, or similar domains involving resource allocation, routing, or positioning decisions
Demonstrated ability to translate complex business problems into mathematical optimization frameworks and work with data science teams to develop and validate solutions
Bachelor's degree in Computer Science, Data Science, Operations Research, Industrial Engineering, Mathematics, Statistics, or related quantitative field required; Master's or PhD preferred
Hands-on experience with AI/ML concepts including feature engineering, model selection, hyperparameter tuning, and understanding of when to use different modeling approaches (regression, classification, optimization, reinforcement learning)
Strong analytical skills with ability to work with large datasets, define data contracts, and ensure data quality for model inputs
Proven ability to drive technical product strategy and roadmaps for AI-powered platforms, balancing short-term MVPs with long-term architectural vision
Experience measuring business impact of AI/ML products through clear KPIs and demonstrating ROI to senior leadership
Excellent technical communication skills with ability to explain complex optimization models and AI systems to non-technical business stakeholders and translate business requirements into technical specifications
Demonstrated ability to influence and align senior stakeholders including VP and C-level executives on technical investment decisions and AI strategy
Experience working in highly cross-functional, matrixed environments requiring collaboration across product, engineering, data science, and business teams
Self-starter with strong ownership mentality and ability to drive results in ambiguous, greenfield problem spaces

Nice To Haves

Familiarity with retail, supply chain, or inventory management systems preferred but not required - strong technical product leaders from other domains welcome

Responsibilities

Develop mathematical optimization models for inventory positioning decisions that maximize expected value by evaluating selling probability, inventory projections, capacity constraints, price trajectories, transportation times, and operational costs across our network
Work hands-on with data scientists on model architecture, feature engineering, algorithm selection, and validation methodologies for production AI systems
Build and validate optimization algorithms for dynamic allocation, replenishment, rebalancing, and recovery positioning including cross-banner returns and clearance inventory optimization
Conduct backtesting, measure model performance using precision/recall/accuracy metrics, and iterate on model design to achieve business outcomes including cycle time reduction, improved recovery value, and margin optimization
Make technical architecture decisions on optimization frameworks, data contracts, model integration patterns, and how AI agents coordinate to make autonomous decisions
Partner with engineering teams to define technical requirements for real-time data pipelines, model serving infrastructure, and integration with warehouse management systems, transportation systems, and inventory platforms
Design and validate A/B testing strategies to measure model impact on business metrics including inventory turns, recovery rates, in-stock improvement, and financial outcomes
Build orchestration capabilities that coordinate specialized AI models (inventory placement, demand forecasting, performance optimization) to make holistic, autonomous inventory decisions
Define model input requirements including demand forecasts, inventory projections, cost structures, capacity constraints, and business rules, working with data engineering teams to ensure data quality and availability
Collaborate with cross-functional stakeholders in Supply Chain, Merchandising, Finance, and Operations to translate business requirements into technical optimization problems and validate model outputs against operational constraints
Develop technical product roadmaps for AI-enabled inventory capabilities, balancing quick wins with long-term platform vision
Lead technical discussions with data science teams on model selection (regression vs. classification vs. optimization), feature importance, hyperparameter tuning, and production deployment strategies
Build trust mechanisms and explainability frameworks for AI recommendations to enable business users to understand and adopt model-driven decisions
Define and track technical KPIs for model performance (latency, accuracy, precision, recall, business metrics) and establish monitoring and alerting for production AI systems
Drive technical discovery and feasibility assessments for new AI use cases, evaluating build vs. buy decisions and determining technical approach for complex optimization problems