Amazon is looking for motivated and innovative individuals with strong analytical skills and practical experience to join our Middle Mile Planning Research and Optimization Science (mmPROS) team. We are hiring specialists at all levels for our scientific team with expertise in transportation and systems engineering, network and combinatorial optimization, stochastic optimization, algorithm design, pricing and yield management, machine learning and demand forecasting.
Middle Mile Air and Ground transportation represents one of the fastest growing logistics areas within Amazon. Amazon Fulfillment Services transports millions of packages via air and ground and continues to grow year over year. Our team (mmPROS) is charged with research and developing models, algorithms, data structures, and data representations for Amazon Delivery Technology (surface and air transportation) businesses globally. Develop optimization-based decision support for planning, routing, operations, pricing, topology, and resource planning.
These tools rely heavily on mathematical optimization, stochastic simulation, meta-heuristic and machine learning techniques. In addition, Amazon often finds existing techniques do not effectively match our unique business needs which necessitates the innovation and development of new approaches and algorithms to find an adequate solution.
As a Data Scientist focusing on research for Seller Fullfilment Services, you will play a key role in Amazon’s transportation and logistics strategic planning and operations. You will build systems and tools that enable us to provide our customers with the largest selection of merchants at the lowest price, and the most reliable delivery service regardless of the seller. You will research, design and improve on the models that will impact Amazon’s customer directly. You will be working in a highly collaborative environment partnering with various science, product management, engineering, operations, finance, business intelligence and analytics teams to develop ML models and analytical tools. You will need to understand the business requirements and translate them into complex analytical outputs. You will design tests to explain performance of the models from impact on customer and cost perspective. You will create ML models to capture features impacting performance. You should be comfortable building prototypes, testing and improving them given the feedback from the real time data. You should be able to present your model and findings to a various range of stakeholders.
· Masters in quantitative field (Computer Science, Mathematics, Machine Learning, AI, Statistics, or equivalent)
· 2 years of experience working in data science in a consumer product company, managing Machine Learning Scientists, Data Scientists, Research Scientists, Applied Scientists, and/or Economists
· Ability to distill informal customer requirements into problem definitions, dealing with ambiguity and competing objectives
· Excellent capability working and manipulating data and developing software application in programming languages such as Python
· Good communication skills with both technical and business people
· Experience designing/implementing machine learning algorithms tailored to particular business needs and tested on large datasets
· Experience in data mining and using databases in a business environment with large-scale, complex datasets.
· PhD in quantitative field (Computer Science, Mathematics, Machine Learning, AI, Statistics, or equivalent)
· 5 years of experience working in data science in a consumer product company, managing Machine Learning Scientists, Data Scientists, Research Scientists, Applied Scientists, and/or Economists
· Experience with modeling sequential data, statistical forecasting, and time series models
· Familiarity with optimization models such as Linear Programming and Integer Programming
· Strong personal interest in learning, researching, and creating new technologies with high customer impact
Amazon is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Vet