Senior Data Analyst, Labor Operations

About the role

This role sits in Stord's Data team and owns the analytics product layer for our Labor Management System. The LMS analytics layer is being built now, alongside the product and engineering team actively developing the LMS itself.

Responsibilities

• Own the end-to-end analytics layer for Stord's Labor Management System: requirements, build, maintenance, and quality
• Act as the primary interface between the Data team and the Operations org for all LMS analytics - you translate operational needs into data product decisions without the business having to prescribe the solution
• Own the reliability of LMS data feeds into the analytics platform - when a building GM says the numbers look wrong, you are the first call
• Work closely with the LMS product manager and engineering team as a core partner - you'll be in the room when new features are scoped, raising data observability requirements before they're built in, not retrofitting analytics after the fact
• Partner with data engineering to ensure the upstream data pipeline supports the accuracy and timeliness that operational dashboards require
• Build and maintain the reporting layer that enables the Operations team to do their own weekly performance analysis - weekly OPH summaries, site comparisons, and trend views
• Design and own the decomposition framework that separates genuine productivity gains from brand mix shifts, volume changes, and order complexity effects - so the Operations team can answer the "why did OPH change" question themselves
• Ensure the data and tooling is reliable and consistent enough that the Operations analytics team is not blocked or dependent on you to interpret results
• Methodology and Metric Ownership: Own how we define and calculate OPH, UPH, UPO, labor utilization, and related KPIs
• Design and maintain the analytical framework that attributes OPH changes to their root causes
• Document definitions and methodology so the broader team understands what the numbers mean
• Data Quality and Integrity: First line of defense on LMS data issues: system migrations, source reconciliation, anomaly detection
• Flag, document, and recommend handling for data irregularities (e.g., hours charged with no shipments)
• Partner with data engineering to ensure LMS and WMS data flows are reliable and well-understood

Requirements

• Track record of working as the interface between a data or analytics team and an operational business unit - you've been the person the business trusts to understand their problems without being walked through requirements.
• Could you sit with a building GM for 30 minutes, understand what's driving their decisions, and come back with a dashboard spec - without your manager or their manager in the room?
• 3-6 years of experience in operations analytics with direct exposure to fulfillment center, 3PL, or warehouse operations
• Fulfillment center or 3PL building experience is key
• Industrial engineering, operations research, or a quantitative supply chain background is a strong plus, particularly where it included hands-on analytics work
• Strong SQL - comfortable querying raw operational data from an LMS, WMS, or equivalent without waiting for a pre-built dataset
• Visualization proficiency - Tableau, Power BI, or equivalent; can build a production-quality dashboard from scratch, not just edit existing templates
• Analytical methodology depth - you've designed decomposition analyses, attribution frameworks, or waterfall analyses; you understand the difference between mix effects and rate effects
• Operational fluency - OPH, UPH, UPO, and labor utilization are concepts you've worked with on the floor, not just in a textbook
• Bias toward rapid delivery - you prototype quickly and iterate, rather than seeking a perfect solution before showing your work
• AI First mentality - Stord is an AI first company. Our team uses AI to write code, do analysis, and summarise / present results
• Strong preference for background in fulfillment operations analytics at a major 3PL or a large-format retailer
• Python for analysis (pandas, numpy, data wrangling)
• Familiarity with Labor Management Systems: Manhattan Active WM, Infor WFM, Kronos/UKG, Blue Yonder, or similar
• Analytics engineering exposure (dbt, lightweight transforms, building reusable data models)
• Multi-site fulfillment network context - you've compared building-level performance and explained variance across sites to senior leadership

Qualifications

• Must have a bachelor's degree in a relevant field such as operations research, industrial engineering, computer science, or a related field
• Experience with SQL and other data analysis tools
• Strong communication and interpersonal skills
• Ability to work independently and manage multiple projects simultaneously
• Experience with Tableau, Power BI, or similar visualization tools
• Understanding of labor management systems and warehouse operations
• Experience with Python and pandas
• Experience with dbt and lightweight transforms
• Experience with multi-site fulfillment networks