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.

With Stord, brands can increase cart conversion, improve unit economics, and drive sustained customer loyalty. Stord’s end-to-end commerce solutions combine best-in-class omnichannel fulfillment and shipping with leading technology to ensure fast shipping, reliable delivery promises, easy access to more channels, and improved margins on every order.

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.

This is your chance to own the entire data function that powers the next generation of AI-driven logistics.

We're looking for a visionary data leader who can unify data engineering, analytics, and data science into a single, coherent function that serves as the supply side of intelligence for all of Stord. You'll scale and advance our data platform while building the data science capabilities that feed real-time AI features across the product.

You won't just run a data engineering team - you'll set the data strategy, lead a multidisciplinary team of engineers, analysts, and scientists, and define how Stord turns raw data into competitive advantage.

What makes this role transformational:
- Unified data ownership: Lead data engineering, data analytics, and data science as one coherent function - eliminating the silos that slow AI-driven companies down
- Platform at scale: Advance and scale our cloud-native data platform to meet the demands of a high-growth, AI-first business processing billions in commerce
- Data science strategy: Own the modeling agenda - from demand forecasting and EDD prediction to experimentation frameworks and feature engineering
- ML-ready data infrastructure: Build the pipelines, feature stores, and training infrastructure that the AI Engineering team consumes to ship product
- Executive influence: Shape technical and organizational data strategy with direct reporting to SVP Product & Engineering

How the org works around this role:
You own the supply side - clean data, reliable pipelines, accurate models, and the infrastructure that produces them. The AI Engineering team and AI product managers own the demand side - prioritizing and turning those assets into shipped product features. The boundary between data science (model building) and AI engineering (model deployment and serving) is a clean, intentional interface, not a grey area.

About the Director of Data Position

This is your chance to own the entire data function that powers the next generation of AI-driven logistics.

We're looking for a visionary data leader who can unify data engineering, analytics, and data science into a single, coherent function that serves as the supply side of intelligence for all of Stord. You'll scale and advance our data platform while building the data science capabilities that feed real-time AI features across the product.

You won't just run a data engineering team - you'll set the data strategy, lead a multidisciplinary team of engineers, analysts, and scientists, and define how Stord turns raw data into competitive advantage.

What makes this role transformational:

• Unified data ownership: Lead data engineering, data analytics, and data science as one coherent function - eliminating the silos that slow AI-driven companies down

• Platform at scale: Advance and scale our cloud-native data platform to meet the demands of a high-growth, AI-first business processing billions in commerce

• Data science strategy: Own the modeling agenda - from demand forecasting and EDD prediction to experimentation frameworks and feature engineering

• ML-ready data infrastructure: Build the pipelines, feature stores, and training infrastructure that the AI Engineering team consumes to ship product

• Executive influence: Shape technical and organizational data strategy with direct reporting to SVP Product & Engineering

How the org works around this role: You own the supply side - clean data, reliable pipelines, accurate models, and the infrastructure that produces them. The AI Engineering team and AI product managers own the demand side - prioritizing and turning those assets into shipped product features. The boundary between data science (model building) and AI engineering (model deployment and serving) is a clean, intentional interface, not a grey area.

What You'll Do

Own the Full Data Function

• Scale and advance our cloud-native data platform, driving architectural improvements that keep pace with rapid business growth

• Architect data systems that serve both BI and ML workloads at increasing scale without sacrificing reliability or governance

• Establish data governance, quality, and lineage frameworks that support compliance and rapid feature development

• Serve as the single executive accountable for data ROI - what gets built, in what priority, measured by business impact

• Coordinate distributed analyst governance across business units via dotted-line relationships with embedded analysts

Lead Data Science Strategy

• Own the data science agenda - define where predictive modeling and statistical analysis create the most leverage

• Drive delivery of core ML use cases: demand forecasting, EDD prediction, routing optimization, and warehouse intelligence

• Establish experimentation frameworks that let the business run rigorous A/B tests and learn faster

• Build feature engineering practices in collaboration with data engineering, ensuring data scientists have clean, model-ready inputs

• Partner with the Decision Science PM to translate business problems into modeling priorities and communicate model outputs to stakeholders

Build ML-Ready Data Infrastructure

• Design and implement real-time streaming architectures that generate ML features at the freshness AI Engineering requires

• Build and maintain feature stores that give data scientists and AI engineers consistent, versioned access to production features

• Create training data pipelines that allow models to retrain reliably with high-quality, well-governed data

• Define the interface between this team (feature generation, training data quality) and AI Engineering (model serving and deployment) - making handoffs clean and scalable

• Ensure data infrastructure supports embedded analytics serving hundreds of customers with real-time operational insights

Lead M&A Data Integration

• Assess data quality, infrastructure maturity, and integration complexity

• Lead post-acquisition data integration, migrating acquired datasets into Stord's platform without disrupting existing operations

• Establish repeatable playbooks for absorbing new data sources, schemas, and business logic from acquired companies

• Partner with engineering and business stakeholders to prioritize which acquired data unlocks the most value, and sequence integration accordingly

Scale a High-Performance Multidisciplinary Team

• Lead data engineering, data analytics, and data science as one team with a shared platform and a unified strategy

• Build hiring and development frameworks that scale from today's 12-18 to a larger org as Stord grows

• Create career paths across three distinct disciplines - engineers, analysts, and scientists - while maintaining a cohesive team culture

• Develop the next generation of data leaders within the org; this role is designed to be a stepping stone for future directors of data engineering and data science

Drive Direct Business Impact

• Partner with AI product managers across three tracks (AI Product, Decision Science, Internal Enablement) to align data priorities with product delivery

• Enable demand planning, EDD prediction, and warehouse optimization features by ensuring the underlying data and models are production-ready

• Build data products that contribute directly to revenue growth, operational efficiency, and customer retention

• Establish SLAs and monitoring ensuring 99.9%+ uptime for business-critical data and model serving systems

What You'll Need

Track Record of Building Data Functions, Not Just Data Teams

• 10+ years of data leadership with proven ability to run multidisciplinary teams spanning engineering, analytics, and data science

• Full-stack data expertise: You've led both a data engineering function and a data science function - ideally at the same time

• Cloud-native architecture experience: Deep expertise with GCP, BigQuery, and modern data stack technologies

• Team scaling success: You've grown high-performing data organizations through hypergrowth phases without losing quality or culture

• Executive partnership: Track record of translating technical and scientific work into business impact at the C-level

The Technical Depth We Need

• Modern data stack mastery: Expert-level experience with BigQuery, dbt, streaming platforms, and BI tools

• Data science fluency: You don't need to write the models, but you need to lead the people who do - strong understanding of ML concepts, experimentation, and model lifecycle management

• Feature store and ML infrastructure knowledge: Hands-on understanding of how to build and operate the data infrastructure that data scientists and ML engineers depend on

• Real-time systems: Deep understanding of streaming architectures, CDC, and low-latency data processing

• Data governance at scale: Experience implementing data quality, lineage, and compliance frameworks across multiple teams

• M&A data experience: Familiarity with data due diligence, integration planning, and absorbing new data sources from acquisitions

The Leadership Mindset We're Looking For

• Integrator, not silo builder: You see data engineering, analytics, and science as one function, not three separate empires

• Architect's vision: You design systems and team structures that scale 10x beyond current needs

• Business translator: You can explain modeling trade-offs to a CFO and infrastructure trade-offs to a CPO

• Team builder: You attract top talent across three different hiring profiles and create environments where each discipline does its best work

• AI-forward thinking: You understand the difference between a BI data warehouse and an AI-first data platform, and you've built the latter

What Gets Us Really Excited

• You've run a unified data function at a high-growth company - engineering and science under one roof

• You have hands-on experience with both traditional EDW (Snowflake, BigQuery) and modern ML infrastructure (feature stores, training pipelines)

• You've built real-time data systems supporting customer-facing applications with strict SLA requirements

• You've defined the interface between a data science team and a model deployment team - and made it work in practice

• You've led data integration work through M&A activity - ideally multiple acquisitions at different scales

• You have experience in logistics, commerce, supply chain, or high-volume operational data domains