Applied Scientist - Optimization, Amazon Transportation

Amazon's Middle Mile Surface Research Science seeks an Applied Scientist to invent and build optimization models and algorithm to improve how Amazon plans and operates its transportation network.

Amazon's transportation network moves millions of truckloads of freight between vendors, warehouses, and customers using a fleet of trucks, trains, and airplanes, on time and at low cost. Operating it requires constant decisions about how to route, schedule, and balance capacity across the network, and our strategy is to make those decisions with science-driven technology. Because existing techniques rarely fit Amazon's scale and unique business needs, this role centers on inventing new approaches and algorithms.

As an Applied Scientist, you'll develop optimization models and algorithms. Your role will initially focus on driver capacity optimization. Your models will impact business decisions worth billions of dollars and improve the delivery experience for millions of customers.

• Design and develop optimization models and algorithms that enhance our optimization and planning systems.
• Build models and algorithms from prototype to production-level systems.
• Translate ambiguous business problems into modeling approaches, and drive the technical design with product, engineering, and operations partners.
• Influence key business decisions through rigorous modeling and analysis.
• Communicate results and recommendations to scientific and business audiences.

• Analyze data to investigate a business problem or model performance and identify improvements
• Brainstorm new algorithmic strategies or business opportunities with fellow scientists
• Leverage GenAI to build and test your new model features
• Run a simulation or experiment to evaluate your model's performance
• Meet with product and tech partners to review project requirements, data, design, or other project decisions
• Review code changes or a design document from a fellow scientist or engineers
• Write and present a paper documenting algorithm features, results, and recommendations

Middle Mile Surface Research Science builds the models and algorithms that plan and operate Amazon's middle mile network. Our work spans operations research, optimization, and machine learning. We work on vehicle route planning, capacity planning, scheduling, network design, transit-time prediction, demand forecasting, and equipment re-balancing. Our team of about ten scientists is part of a broader science organization whose scientists bring deep expertise in machine learning and optimization.

We optimize decisions worth billions of dollars and reach millions of customers.

• PhD, or Master's degree and 4+ years of science, technology, engineering or related field experience
• 1+ years of programming in Java, C++, Python or related language experience
• Experience building machine learning models or developing algorithms for business application
• Experience in optimization mathematics such as linear programming and nonlinear optimization

• Experience in professional software development
• Experience in standard machine-learning and statistical modeling tools and techniques (e.g. random forests, gradient-boosted regression, LASSO, logistic regression)
• Experience with probability, statistics, and optimization under uncertainty
• Experience with advanced mathematical programming techniques such as column generation, cuts, or benders decomposition
• Experience with meta-heuristic optimization techniques, such as iterative local search or genetic algorithms
• Experience implementing high-performance algorithms such as shortest paths, network flow algorithms, or local search algorithms

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

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