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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and developing digital twin models and AI-driven decision systems to optimize warehouse operations and improve operational performance. Proficient in advanced data science techniques, including simulation, forecasting, and optimization within complex supply chain environments.
Highest-signal resume keywords
Digital Twin DevelopmentDiscrete-Event SimulationMachine Learning DeploymentPython ProficiencyData Science Expertise
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
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Hard Skills
Data ScienceOperations ResearchForecasting ModelsInventory OptimizationStatistical ValidationTask PrioritizationException HandlingAutomation WorkflowsSlotting AlgorithmsMulti-Pass Picking
Tools & Technologies
PythonRSQLAnyLogicSimioFlexSim
Certifications & Qualifications
Master’s DegreePhD
Industry Keywords
Supply ChainLogisticsManufacturingOperational EnvironmentsAI-Driven Operations
Tech Stack
Tools & technologiesPythonSQL
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