Senior Data Analyst, Inventory Visibility and Controls (Hybrid, Seattle)

Nordstrom•Seattle, WA

3d•$121,000 - $199,000•Hybrid

About The Position

The Inventory Visibility & Controls team (IV&C) was established to strengthen enterprise-wide inventory transparency, traceability, and controls, scaling data, analytics, and AI-powered insights that enable proactive decision-making, prevent loss and unsellable inventory, and support foundational capabilities such as RFID and inventory lifecycle management. We are hiring a Senior Data Analyst for Inventory Visibility & Controls within Inventory Operations . In this role, you will help scale enterprise inventory visibility and controls by translating complex business questions into decision-ready insights and durable analytics products that strengthen traceability across the product lifecycle. In addition to delivering insights, this role will play a key part in advancing automation‑first and AI‑enabled analytics by partnering with product and engineering teams to scale real‑time visibility, anomaly detection, and human‑in‑the‑loop resolution workflows, embedding insights directly into tools that drive action—not just awareness. You will partner closely with business leaders and cross‑functional teams across program management, product, engineering, and analytics to define requirements, acquire and transform data, and deliver dashboards, analyses, and narratives that enable faster anomaly detection and resolution, reduce risk, and unlock unintentionally unsellable inventory. Additionally, you’ll contribute to a culture of data-driven decision-making through strong storytelling, stakeholder engagement, and data literacy—flexing across priorities as needed to support the team’s highest-impact visibility and controls outcomes. This role is ideal for a senior analyst who thrives at the intersection of data, operations, and controls and wants to reduce manual investigation, accelerate time to resolution, and help shift IV&C from reactive reporting to proactive, automated, and AI‑assisted inventory controls at enterprise scale.

Requirements

Bachelor’s degree in a quantitative field (e.g., Analytics, Statistics, Computer Science, Economics, Engineering); advanced degree or relevant certification strongly preferred.
5+ years of professional experience delivering advanced analytics, data products, and business insights in complex, cross‑functional environments.
Expert‑level SQL skills, with experience writing clean, repeatable, production‑ready code and operating in production analytics environments including version control, deployment standards, and data quality monitoring.
Advanced experience acquiring, transforming, joining, and blending data across multiple internal and external sources, with deep understanding of data structures, quality considerations, and trade‑offs.
Expert knowledge of at least one programming language (e.g., Python, R) to support analysis, modeling, automation, and production workflows; working knowledge of additional languages and tools.
Proven ability to design and execute end‑to‑end analytical workflows, applying advanced statistical, analytical, and (where applicable) predictive techniques to solve ambiguous business problems.
Experience designing or supporting automated analytics, monitoring, or alerting solutions, with exposure to AI‑assisted or conversational analytics in enterprise self‑service or investigation workflows.
Exceptional analytical storytelling and communication skills, with a track record of influencing decisions by clearly articulating insights, risks, opportunities, and trade‑offs to both technical and non‑technical audiences.
Advanced data visualization expertise (e.g., Looker or similar tools), including the ability to design cohesive dashboard suites, elevate visualization standards, and deliver intuitive, self‑service experiences at scale.
Strong business acumen with the ability to deeply understand domain‑specific processes, metrics, and systems, and proactively identify where analytics can drive the greatest impact.
Demonstrated ability to build trusted partnerships with senior stakeholders, operate as a strategic thought partner, and independently drive work forward in highly ambiguous environments.
Experience with data governance, metric standardization, and production analytics best practices strongly preferred.
Experience with inventory, merchandising, supply chain, or operational data and KPIs is a plus.

Nice To Haves

advanced degree or relevant certification strongly preferred.
Experience with data governance, metric standardization, and production analytics best practices strongly preferred.
Experience with inventory, merchandising, supply chain, or operational data and KPIs is a plus.

Responsibilities

Own and lead the delivery of high ‑ impact, production ‑ ready data products that support enterprise inventory visibility, anomaly detection, and controls across the inventory lifecycle —including complex analyses, scalable dashboards, and executive‑level insights—across ongoing and ambiguous, high‑stakes problem spaces.
Translate loosely defined business questions into clear analytical strategies , independently designing end‑to‑end approaches that identify the right data, methods, trade‑offs, and success measures.
Partner with product, data science, and engineering teams to shape AI‑enabled analytics , including anomaly detection, prioritization, and recommendation experiences that support faster, more consistent inventory issue resolution.
Contribute to human‑in‑the‑loop AI workflows , ensuring analytical logic is explainable, auditable, and aligned with inventory control and financial integrity standards as automation scales.
Design and maintain trusted analytical assets by driving standards for data modeling, metric definitions, data quality, and governance—ensuring insights are durable, explainable, and aligned to one source of truth.
Lead analytical storytelling for complex and conflicting findings , connecting data across domains to provide context, highlight risks and opportunities, and influence decisions at multiple leadership levels.
Anticipate downstream impacts and stakeholder reactions , effectively landing insights that may challenge assumptions while maintaining trust and momentum toward action.
Mentor and elevate junior analysts , contributing to shared best practices, technical standards, and team learning to raise the quality and consistency of analytical work.
Continuously identify opportunities to improve analytics tooling, architecture, and processes, with a focus on automation, AI‑assisted insights, and scalable self‑service experiences that move the organization from reactive reporting to proactive control.