The NA AMZL Supply Chain organization leads the innovation of Amazon’s Last Mile. We are an Operations org that hires and manages associates to deliver packages next day and sub-same day. The Execution and Planning Science (EPS) team sits within NA AMZL Supply Chain with the mission to build world-class automated Science-Tech products that enable ultra-fast delivery speeds for Amazon customers and job market opportunities for Amazon associates. Our key vision is to transform the online experience. We’re growing in scale and volume, by orders of magnitude. You will develop Science-Tech solutions to craft business strategy and roadmap to enable some of Amazon’s biggest brands to delight millions of customers. To succeed, we need senior technical leaders to forge a path into the future by building innovative, maintainable, and scalable systems.

At Amazon, we are constantly inventing and re-inventing to be the most associate-centric company in the world. To get there, we need exceptionally talented, bright, and driven people. Amazon is one of the most recognizable brand names in the world and we distribute millions of products each year to our loyal customers.

We are looking for a Data Scientist to build ML prediction models. They will own contained analyses and insights as well as building models for production, typically which will be inputs to optimization models. This team plays a significant role in various stages of the innovation pipeline from identifying business needs, developing new algorithms, prototyping/simulation, to implementation by working closely with colleagues in engineering, product management, operations, retail and finance.

Minimal travel requirements. Quarterly site visits or team off-sites are typical.

Key job responsibilities
- Leverage advanced statistical and machine learning techniques to analyze large and complex datasets.
- Collaborate with cross-functional teams to identify business problems and develop innovative data-driven solutions.
- Communicate findings effectively to both technical and non-technical stakeholders through compelling data visualizations and presentations.
- Collaborate with business teams to understand requirements and translate them into actionable insights.
- Develop production software systems utilizing advanced algorithms to solve business problems.
- Proactively identify interesting areas for deep dive investigations and future product development.
- Design and execute experiments, and analyze experimental results in collaboration with Product Managers, Business Analysts, Applied/Research Scientists, and other specialists.
- Partner with data engineering teams across multiple business lines to improve data assets, quality, metrics and insights.
- Leverage industry best practices to establish repeatable applied science practices, principles & processes.