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Stord

Lead Data Scientist

Stord

Lead Data Scientist for Stord Labs focusing on digital twin models and AI-driven decision systems in logistics. Partnering with engineers and operations specialists to innovate in warehouse workflows.

Posted 7/2/2026full-timeRemote • 🇺🇸 United StatesSeniorWebsite

Tech Stack

Tools & technologies
PythonSQL

About the role

Key responsibilities & impact
  • 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.

Requirements

What you’ll need
  • 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.

Benefits

Comp & perks
  • Health insurance
  • Professional development opportunities

ATS Keywords

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Hard Skills & Tools
Data ScienceOperations ResearchForecasting ModelsInventory OptimizationStatistical ValidationTask PrioritizationException HandlingAutomation WorkflowsSlotting AlgorithmsMulti-Pass Picking
Certifications
Master’s DegreePhD