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Stord, Inc. is a global leader in cloud supply chain technology and expertise. The company is committed to improving supply chains by relying on the cloud to he
Director of Data
Location
United States
Posted
107 days ago
Salary
0
Seniority
Lead
Job Description
Director of Data
Stord, Inc.
• 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 • 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 • 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 • 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 • 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 • 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
Job Requirements
- 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
- 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.
Benefits
- Health insurance
- 401(k) matching
- Flexible work hours
- Paid time off
- Professional development opportunities
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