Associate Data - New Grad

About Stord

Stord is a consumer experience platform that helps brands create a smooth path from checkout through final delivery. The company is growing quickly and is working toward doubling revenue over the next 18 months. To support that goal, Stord is expanding teams across the business and looking for motivated people who can help advance its mission.

By pairing commerce-enablement software with large-scale fulfillment services, Stord gives brands the tools to compete with major retail players. The company supports more than $10 billion in commerce each year through fulfillment, warehousing, transportation, and its software stack, which includes OMS, pre- and post-purchase tools, and WMS. Its goal is to help brands deliver a strong consumer experience at scale.

Brands using Stord can improve conversion, strengthen unit economics, and build customer loyalty over time. The company combines omnichannel fulfillment and shipping with technology designed to support fast delivery, dependable promises, broader channel access, and better margins on every order.

Well-known DTC and B2B brands such as AG1, True Classic, Native, Seed Health, quip, goodr, and Sundays for Dogs rely on Stord for customer experience on every order. The company is based in Atlanta and operates facilities across the United States, Canada, and Europe. It is supported by investors including Kleiner Perkins, Franklin Templeton, Founders Fund, Strike Capital, Baillie Gifford, and Salesforce Ventures.

Role Overview

This early-career data role is designed for new graduates who want to work on real business data rather than prebuilt reporting. Stord is hiring into three possible paths within its data organization: Analyst, Engineer, and Scientist. Applicants apply once, share their strongest area, and are matched to the track that best fits their background.

The company’s supply chain platform brings together Order Management, Warehouse Management, Transportation, and Consumer Experience for brands processing over $10 billion in annual commerce. The data is large-scale, connected, and operationally important, covering shipments, inventory, order movement, warehouse throughput, carrier performance, and more.

Work Location

This position is based in Stord’s Atlanta office at headquarters near Hartsfield-Jackson Airport in Union City, GA. The role requires working onsite at least 3 days each week.

Being close to operations is considered important for success in this role. The office location provides direct exposure to the warehouse floor, operations teams, and product teams that generate and use the data, helping you produce sharper analysis and more grounded models.

Track Responsibilities

Data Analyst: Turn data into business decisions by working with product, operations, and leadership teams to understand performance, identify root causes, and recommend actions.

• Create and support dashboards and reporting that teams actively rely on
• Use SQL to answer business questions quickly and with precision
• Investigate unusual metric behavior and determine what changed
• Convert findings into practical recommendations for non-technical partners
• Define and monitor KPIs for product areas and operational processes
• Work with Data Engineers to keep the data reliable, accessible, and clean

Data Engineer: Build the systems and pipelines that move data from source systems into trusted, queryable datasets used across the business.

• Develop and maintain scalable data pipelines for ingestion, transformation, and delivery
• Create and document dbt models that support analytics and reporting
• Set and uphold data quality standards for downstream reliability
• Collaborate with engineering teams to add new data sources as products launch
• Support Analysts and Scientists with the infrastructure they need
• Contribute to platform tooling, orchestration, and observability

Data Scientist: Build statistical and machine learning models that improve how the platform and operations perform.

• Design and deploy predictive models that drive better operational and product outcomes
• Partner with Analysts to move from descriptive work to predictive and prescriptive insights
• Plan experiments and evaluate the effect of product or operational changes
• Explain modeling results in a way that action-oriented teams can use
• Work with Data Engineers to bring models into production
• Stay informed on applied machine learning approaches relevant to supply chain and logistics

Core Qualifications

Across all three tracks, the ideal candidate brings a strong analytical base and the ability to contribute independently while learning quickly.

• Strong SQL capability, including writing queries directly and understanding how they work
• Working knowledge of Python for analysis, scripting, or modeling depending on the track
• Analytical discipline and the habit of validating assumptions before drawing conclusions
• Clear written and verbal communication for technical and non-technical audiences
• Ownership-oriented mindset with a focus on delivering real business impact
• Comfort using AI tools to work more efficiently without overrelying on them

Track-Specific Strengths

Analyst: Strong instincts for data visualization, experience with BI tools such as Looker, Tableau, Mode, or similar platforms, and comfort presenting findings to non-technical audiences.

Engineer: Familiarity with data pipeline concepts, exposure to dbt, Airflow, Spark, or cloud data warehouses such as Snowflake, BigQuery, or Redshift, along with software engineering fundamentals.

Scientist: Coursework or project experience in statistics, machine learning, or applied modeling; comfort with scikit-learn, pandas, or similar tools; and the ability to turn a business issue into a modeling problem.

What Success Looks Like

30 days: You understand the data environment, know the key metrics your team monitors, and have contributed to at least one live deliverable. You ask thoughtful questions and take initiative without waiting to be assigned every task.

90 days: You are handling analyses, pipelines, or models with limited oversight. The teams you support trust your work, and you have identified at least one insight or data quality issue that influenced a decision.

6 months: You are a dependable, independent contributor with informed views on how the data organization can improve and the credibility to support those views.

Why Consider Stord

• You will work with live data that affects real operational decisions, not synthetic datasets or dashboards that go unused
• You will collaborate across product, engineering, operations, and leadership, building business understanding faster than many other data roles
• The company uses AI tools as part of everyday work and expects team members to do the same
• The team is building its data infrastructure now, so your decisions can shape how the organization uses data for years to come