Data Scientist Amazon
Seattle, WA

Job Description

Are you interested in innovating to deliver a world-class level of service to Amazon's selling partners? The Selling Partner Paid Services (SPPS) team seeks to improve the customer experience on Amazon.com by working directly with sellers on Amazon to improve value, selection and convenience across their business. A new and growing team, our team invents and innovates across technology, processes and people to grow the program, grow our Selling partners' businesses, improve Selling Partner acquisition, retention, engagement and satisfaction and enable scalable global solutions.

The Paid Services Data Science and Analytics team is looking for a data scientist who will work closely with multiple business stakeholders to transform our business by automating recommendations and decision-making by creating various tools and machine-learning models to answer complex business questions that provide insights to influence customer behavior. The Data Scientist must be comfortable working with large volumes of data and be able to manipulate data using a variety of tools. They must be able to build scalable tools to not only process the data but transform it into actionable information.

In addition, the Senior Data Scientist must possess excellent interpersonal skills and be able to provide thought leadership and guidance to the other members within the team.

The role's key responsibilities will include:

* Drive deep understanding of Selling Partner segmentation, behavior and satisfaction, Amazon operational performance and impact on Amazon customer experience by identifying, developing, and executing analyses, machine learning capabilities and models
* Navigate ambiguity; identify and tackle strategic opportunities and problems we don't even know exist or have not fully defined through data.
* Mentor other team members who are looking to improve their understanding of machine learning and data science
* Analyze data for trends and input validity by inspecting univariate distributions, exploring bivariate relationships, constructing appropriate transformations, and tracking down the source and meaning of anomalies
* Apply a range of data science techniques and tools combined with subject matter expertise to solve difficult business problems and cases in which the solution approach is unclear
* Build models using one or more of the following: statistical modeling, mathematical modeling, econometric modeling, network modeling, social network modeling, natural language processing, machine learning algorithms, genetic algorithms, and neural networks.
* Validate models against alternative approaches, expected and observed outcome, and other business defined key performance indicators.
* Implement models that comply with evaluations of the computational demands, accuracy, and reliability of the relevant ETL processes at various stages of production.