Senior Data Scientist, Execution and Planning Science (EPS)

- 5+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
- 4+ years of data scientist experience
- Experience with statistical models e.g. multinomial logistic regression
- 2+ years of data visualization using AWS QuickSight, Tableau, R Shiny, etc. experience
- Experience managing data pipelines
- Experience as a leader and mentor on a data science team

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $143,300/year in our lowest geographic market up to $247,600/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.
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.