About the job
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.
Responsibilities
- 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.
Qualifications
Minimum
- 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
Preferred
- 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