Lead Data Scientist

Job description

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. Stord is launching Stord Labs, an innovation center designed to evaluate emerging logistics technologies and redefine the future of fulfillment. We are 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. Your work will directly inform how innovations scale across Stord’s broader fulfillment network, translating experimental results into enterprise-level operational strategies. You will 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. If you bring deep expertise in simulation, machine learning, and applied analytics—and want to work at the intersection of data science and physical operations—this role is designed for you. What You'll Do: Digital Twin & Simulation Modeling 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. Applied Artificial Intelligence 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. Analytics & Experimentation Validation 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. Academic & Frontier AI Partnerships 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. Basic 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. Bonus Points: Experience operating as a standalone data scientist in an R&D lab, innovation center, startup environment, or advanced manufacturing technology setting. Familiarity with WMS/OMS data structures and warehouse operational datasets. Experience experimenting with large language models (LLMs) or agentic AI systems for workflow automation, exception management, or decision support in operations contexts.