Have you ever placed an order on Amazon and wondered how it got to you so fast? Behind that speed is one of the most complex transportation networks in the world, and the scientific problems it poses are hard, ambiguous, and largely unsolved. We are looking for a senior research scientist who wants to invent the models that run it. Come join the Network Engineering, Scheduling and Technology (NEST) Science team within Amazon Transportation Services. We are looking for a Sr. Research Scientist to lead the design and development of novel mathematical models that govern how Amazon's Middle Mile network operates. You will work on scientifically complex, poorly structured problems in network planning, scheduling, and transportation operations, inventing and extending techniques from operations research, machine learning, stochastic modeling, and simulation to solve them. This is a modeling-first role: your primary output is rigorous, original scientific work that reduces transportation cost and improves customer experience at scale. The ideal candidate has deep expertise in at least one relevant scientific discipline and a track record of taking ambiguous business problems from formulation through to production-grade models. You are comfortable owning the scientific agenda for large problem areas, framing the right questions, inventing solutions where no textbook answer exists, and influencing scientists, engineers, and leaders across multiple teams. Key job responsibilities **Invent** - Invent and design new models and algorithms for scientifically complex problems in transportation network planning, scheduling, equipment selection, and labor optimization - Frame ambiguous, poorly defined business problems into tractable scientific formulations, distinguishing problems that require novel research from those addressable with existing techniques - Extend the state of the art in operations research, optimization, machine learning, and stochastic/discrete-event simulation as the problem demands - Author internal and external peer-reviewed publications where appropriate and not precluded by business considerations **Implement** - Lead the design, implementation, and delivery of production-grade models for complex, system-level problems, working from concept through deployment with engineering partners - Develop code (Python, Java, C++, Julia, or similar) to build, validate, and productionize models at Amazon Code Bar quality where models ship to production - Build models that are maintainable, extensible, accurate, and cost-effective, making principled trade-offs between scientific rigor and speed of execution - Design and run experiments and simulations to validate assumptions, quantify benefits, and gate rollouts on solution quality **Influence** - Drive the scientific agenda for medium-to-large problem areas, proposing new initiatives and securing leadership buy-in - Influence science and engineering strategy across multiple teams, building consensus and resolving contentious technical trade-offs - Mentor and develop other scientists, raise the team's scientific bar, and participate actively in the hiring process - Communicate complex scientific findings and recommendations clearly to technical and non-technical audiences, including senior leadership About the team The Network Engineering, Scheduling, and Technology (NEST) Science Team prototypes, builds, and productionizes mathematical models that reduce transportation cost and improve customer experience in Amazon's Middle Mile network. Equipped with techniques from Operations Research, Machine Learning, and Simulation, these models govern scheduling and equipment selection of hundreds of thousands of truck movements, optimize network configurations, determine transit times between nodes, and simulate network flow under uncertainty for informed decision making. Our core team consists of Applied, Data, and Research Scientists along with technical Product Managers from diverse backgrounds.