Senior Data Analyst, Labor Operations

About the Company

Stord is The Consumer Experience Company, powering seamless checkout through delivery for today's leading brands. Stord is rapidly growing and is on track to double our revenue in the next 18 months. To meet and exceed this target, Stord is strategically scaling teams across the entire company, and seeking energetic experts to help us achieve our mission.

By combining comprehensive commerce-enablement technology with high-volume fulfillment services, Stord provides brands a platform to compete with retail giants. Stord manages over $10 billion of commerce annually through its fulfillment, warehousing, transportation, and operator-built software suite including OMS, Pre- and Post-Purchase, and WMS platforms.

Stord is leveling the playing field for all brands to deliver the best consumer experience at scale.

Hundreds of leading DTC and B2B companies like AG1, True Classic, Native, Seed Health, quip, goodr, Sundays for Dogs, and more trust Stord to deliver industry-leading consumer experiences on every order. Stord is headquartered in Atlanta with facilities across the United States, Canada, and Europe.

Stord is backed by top-tier investors including Kleiner Perkins, Franklin Templeton, Founders Fund, Strike Capital, Baillie Gifford, and Salesforce Ventures.

Our fulfillment buildings process tens of thousands of orders daily across an ever expanding network. The data that comes out of those buildings - labor performance, efficiency trends, brand-level throughput - is central to how we run the business and how we retain and grow our brand relationships. Analytics is a competitive advantage for us, and we're investing in the people who can unlock it.

About the Role

This role sits in Stord's Data team and owns the analytics product layer for our Labor Management System. What makes it genuinely interesting: you're not inheriting a legacy setup - you're building alongside the team actively developing the LMS as a product. The Operations org is your customer.

Your job is to understand what building GMs and area managers need from their data, and deliver it without needing them to hand-hold you through requirements. The need this role fills is specific: operational fluency combined with technical execution.

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.
• When a data quality issue surfaces, you’ll have enough technical credibility to go directly to LMS engineering and distinguish an analytics pipeline problem from a source system problem - and get it resolved.
• 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.
• Own how we define and calculate OPH, UPH, UPO, labor utilization, and related KPIs.
• Document definitions and methodology so the broader team understands what the numbers mean.
• 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).

Qualifications

• 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.
• 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.

Preferred Skills

• 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.