Amazon Applied Scientist - Optimization Decision Sciences Cognitive Science Team 

United States, Washington, Seattle 

477385845

In this role, you will have an opportunity to both develop advanced scientific solutions, and drive critical customer and business impacts. You will play a key role to drive end-to-end solutions from understanding our business requirements, exploring a large amount of historical data, building, managing and improving optimization, simulation and HOTW decision models, building prototypes and products, and exploring conceptually new solutions, to working with partner teams for prod deployment that will be used primarily for Promotion Optimization and Scenario Planning Capabilities for in-period promotion, and long-term financial planning.You will collaborate closely with scientists, engineering peers as well as business stakeholders. You will be at the heart of a growing and exciting focus area for Amazon Devices and Services.You are an individual with avanced analytical abilities, excellent communication skills, and are comfortable working with cross-functional teams and systems. You will be responsible for researching, prototyping, experimenting, analyzing prescriptive optimization models and developing smart automation solutions.
Key job responsibilities
- Develop and maintain optimization frameworks, libraries, and tools to enable scalable and efficient optimization and automation capabilities
- Analyze optimization results, identify bottlenecks, and continuously improve the performance and accuracy of optimization models
- Stay up-to-date on the latest academic research and industry best practices in optimization techniques and tools, and identify opportunities to apply new approaches
A day in the life

- PhD, or Master's degree and 6+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- 3+ years of building machine learning models for business application experience
- PhD in engineering, technology, computer science, machine learning, robotics, operations research, management science, operations management, statistics, mathematics or equivalent quantitative field.

- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.
- Strong core competency in mathematics and statistics.