• 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