• Lead the end-to-end delivery of logistics optimization projects, from problem definition and requirements gathering through model development, validation, deployment, and iteration. • Develop and maintain optimization models using Linear Programming (LP) and Mixed Integer Programming (MIP) to solve real-world logistics, transportation, and network design problems. • Implement optimization solutions using CPLEX, including model formulation, data preparation, solver integration, parameter tuning, and performance optimization. • Write clean, maintainable, production ready Python code for model building, experimentation frameworks, data processing, and service integration. • Collaborate with software engineering teams to integrate optimization models into production applications, ensuring reliability, observability, and maintainability. • Partner with data engineering and analytics teams to access, shape, and validate high-quality datasets using SQL and Snowflake. • Translate complex mathematical and operational concepts into clear insights for non‑technical audiences through effective storytelling and data visualization (e.g., Power BI, Python visualization libraries). • Document model assumptions, constraints, design decisions, and handoff artifacts so engineering teams can operationalize solutions smoothly. • Establish and promote best practices for optimization model development, including versioning, reproducibility, testing patterns, and scalable architecture. • Collaborate cross‑functionally with product, operations, engineering, and business stakeholders to ensure alignment on goals, timelines, and implementation impacts. • Support or lead prototyping of simple web‑based tools (where helpful) to accelerate proof‑of‑concept validation and stakeholder adoption.