Applied Scientist - Machine Learning, Amazon Transportation

Amazon's Middle Mile Surface Research Science seeks an Applied Scientist to invent and build machine learning models that 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 machine learning, forecasting, and prediction models and algorithms. You role will initially develop transit time prediction and uncertainty models. Your models will impact business decisions worth billions of dollars and improve the delivery experience for millions of customers.

- Design and develop machine learning, forecasting, and other prediction models 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.