Lead Data Scientist

This is a fully remote position, open to applicants in United States.

📋 Description

• Oversee the creation and development of digital twin models that effectively mirror comprehensive warehouse operations.

• Gather and organize operational data from the micro-fulfillment lab to construct scalable macro-simulations that can represent enterprise-level environments with thousands of SKUs.

• Conduct stress tests on operational strategies—including slotting algorithms, multi-pass picking, batching logic, and automation workflows—within simulation environments before production rollout.

• Design, evaluate, and implement AI-driven decision-making systems directly into operational processes.

• Create models for forecasting, labor planning, inventory optimization, task prioritization, and exception management to enhance throughput, speed, and cost-effectiveness.

• Develop lightweight, production-ready analytical tools and algorithms that boost operational performance without significant infrastructure demands.

• Convert operational data into financial impact models, linking time-and-motion studies to margin enhancement, productivity improvements, and labor efficiency.

• Collaborate with operations analysts to establish comprehensive experimental frameworks, including success criteria, measurement techniques, and statistical validation methods.

• Examine intricate, multi-variable experiments such as inventory commingling strategies and their effects on density, availability, and fulfillment speed.

• Act as the main technical liaison with external AI organizations, leading model providers, and technology partners.

• Work together with academic institutions to support applied research in simulation, optimization, and AI-driven operations.

⛳️ Requirements

• Master’s degree or PhD in Data Science, Operations Research, Computer Science, Industrial Engineering, or a similarly quantitative field.

• Over 5 years of applied data science experience in supply chain, logistics, manufacturing, or other intricate operational settings.

• Advanced skills in Python, R, and SQL.

• Demonstrated expertise in constructing discrete-event simulations, continuous simulations, or digital twin systems using tools such as AnyLogic, Simio, FlexSim, or custom frameworks.

• Strong history of implementing machine learning and optimization models into live production or operational decision systems.

🏝️ Benefits

• Health insurance

• Professional development opportunities