Lead Data Scientist

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—such as 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.

Upskill

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