Lead Data Scientist

Role Description Stord is seeking a highly skilled Lead Data Scientist to serve as the primary analytics and modeling expert within our core innovation team. Unlike most data science roles in logistics that operate far removed from day-to-day operations, this role is embedded directly in a live fulfillment environment. As Lead Data Scientist for Stord Labs, you will: Build digital twins, develop predictive and prescriptive models, and evaluate agentic AI systems against real-world warehouse workflows inside a dedicated micro-fulfillment facility. Inform how innovations scale across Stord’s broader fulfillment network, translating experimental results into enterprise-level operational strategies. Serve as the technical backbone of a five-person innovation team, partnering closely with controls engineers and operations specialists, and collaborating with frontier AI organizations and academic research partners. Qualifications Master’s degree or PhD in Data Science, Operations Research, Computer Science, Industrial Engineering, or a highly quantitative field. 5+ years of applied data science experience in supply chain, logistics, manufacturing, or other complex operational environments. Advanced proficiency in Python, R, and SQL. Proven experience building discrete-event simulations, continuous simulations, or digital twin systems using tools such as AnyLogic, Simio, FlexSim, or custom frameworks. Strong track record of deploying machine learning and optimization models into live production or operational decision systems. Requirements Lead the design and development of digital twin models that accurately replicate end-to-end warehouse operations. Ingest and structure operational data from the micro-fulfillment lab to build scalable macro-simulations capable of representing enterprise-scale environments with tens of thousands of SKUs. Stress test operational strategies—including slotting algorithms, multi-pass picking, batching logic, and automation workflows—within simulation environments prior to production deployment. Design, test, and deploy AI-driven decision systems directly into operational workflows. Develop models for forecasting, labor planning, inventory optimization, task prioritization, and exception handling to improve throughput, speed, and cost efficiency. Build lightweight, production-ready analytical tools and algorithms that improve operational performance without heavy infrastructure overhead. Translate operational data into financial impact models, linking time-and-motion studies to margin improvement, productivity gains, and labor efficiency. Partner with operations analysts to design robust experimental frameworks, including success criteria, measurement methodologies, and statistical validation approaches. Analyze complex, multi-variable experiments such as inventory commingling strategies and their impact on density, availability, and fulfillment speed. Serve as the primary technical interface with external AI organizations, frontier model providers, and technology partners. Collaborate with academic institutions to sponsor applied research in simulation, optimization, and AI-driven operations. Integrate external research and capabilities into real-world operational testing within fulfillment workflows. Benefits Opportunity to work at the intersection of data science and physical operations. Engagement in innovative projects within a live fulfillment environment. Collaboration with leading AI organizations and academic research partners.