Data Scientist II, Category Strategy and Space science

- 2+ years of data scientist experience
- 3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
- 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
- Experience applying theoretical models in an applied environment
- Experience with machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance
- Experience in a ML or data scientist role with a large technology company

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.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $125,500/year in our lowest geographic market up to $212,800/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.
WWGS (Worldwide Grocery Stores) organization leads the innovation of Amazon’s omni-channel grocery offerings (Fresh, Whole Foods, 3P). In this space, Amazon aims to delight customers by providing broad selection, competitive prices, best-in-class delivery, convenient in-store/pickup options across regions, and an end-to-end shopping experience that makes it easy for customers to discover what they love and build complete grocery baskets. The Grocery Management Science team in WWGS is looking for a Data Scientist to help create and drive the long-term vision for how we manage selection of products for our cross-banner grocery brands globally.

As a Data Scientist on our team, you will work with product managers, vendor managers, software development engineers (SDEs), and other scientists to help the WWGS organization determine optimal selection breadth and depth for our online and physical stores. You will also develop and extend models of key customer behavior and preferences by quantifying expected demand, derive optimal pricing and promotion strategies and optimization approaches that produce selection recommendations that delight our customers and meet our real-world constraints. You will be expected to own the the full lifecycle of your projects from devising methodological approaches, implementation, validation, and working with SDEs to integrate into production systems.

A successful candidate will be able to partner effectively with both business and technical teams, including clear communication of results and the ability to influence a variety of stakeholders. The ideal candidate will posses strong knowledge of causal estimation techniques, and be curious and excited to apply these in challenging real-world settings.


Key job responsibilities
* Interact with engineering, operations, science, and business teams to develop an understand and domain knowledge of processes, system architectures, and business requirements.
* Develop scalable models to generate selection models.
* Create prototypes, simulations, and experiments to test devised solutions.
* Advocate technical solutions to business stakeholders, engineering teams, and executive-level decision makers
* Work closely with engineers to integrate models into production systems.