Senior Data Scientist

Job ID: 1309143 | Services LLC


Amazon's Customer Journey Analytics team creates reporting and analysis for teams across the retail website. As part of the analytics team, you have the opportunity to discover insights that drive changes in how customers shop and interact with the website.

We are seeking an exceptionally curious and customer focused Data Scientist responsible for identifying and solving customer pain points across the Amazon website. This candidate will conduct scientific research, build predictive models and contribute to production ML rankers to enhance the customer experience and drive long term financial value.

A successful candidate will be a self-starter, comfortable with ambiguity, able to think big and be creative (while still paying careful attention to detail). They should be able to translate how data represents the customer experience, be comfortable dealing with large and complex data sets, and have experience using machine learning and econometric modeling to solve business problems. They should have strong analytical and communication skills, be able to work with product managers and software teams to define key business questions and work with the analytics team to solve them.

Key responsibilities include:
· Design, build and automate datasets to scale and support business needs, allowing you to focus on answering hard and ambiguous question
· Identify, develop, manage, and execute research to uncover areas of opportunity and present recommendations that will shape the future of the retail website
· Retrieve and analyze data using a broad set of Amazon’s data technologies and resources, knowing how, when, and which to use.
· Collaborate with product, technical, business, marketing, finance, and UX leaders to gather data and metrics requirements.
· Design, drive and analyze experiments to form actionable recommendations. Present recommendations to business leaders and drive decisions.
· Manage and execute entire projects from start to finish including project management, data gathering and manipulation, synthesis and modeling, problem solving, and communication of insights and recommendations.
· Develop and document scientific research to be shared with the greater data science community at Amazon


· Master’s degree in a highly quantitative field (Machine Learning, AI, Computer Science, Statistics, Mathematics, Operational Research, etc.), or equivalent quantitative field
· At least 5 years of experience with data querying languages (e.g. SQL), scripting languages (e.g. Python), or statistical/mathematical software (e.g. R, Stata, Matlab).
· Experienced in using multiple data science methodologies to solve complex business problems (e.g. statistical analysis, research science, machine learning and deep learning techniques, data modeling, regression modeling, financial analysis, demand modeling, etc.).
· Ability to distill informal customer requirements into problem statements while operating with competing objectives.
· Demonstrable track record of dealing well with ambiguity, prioritizing needs, and delivering results in a dynamic environment.
· Proven ability to communicate verbally and in writing to technical peers and leadership teams with various levels of technical knowledge, educating them about systems and algorithms, as well as sharing insights and data-driven recommendations.


· A PhD degree in a highly quantitative field (Machine Learning, AI, Computer Science, Statistics, Mathematics, Operational Research, etc.), or equivalent quantitative field
· Extensive knowledge and practical experience in several of the following areas: machine learning, statistics, NLP, deep learning, recommendation systems, information retrieval.
· 10+ years of experience working in data science in e-commerce
· Experience processing, filtering, and presenting large quantities (Millions to Billions of rows) of data.
· Experience designing experiments, and ability to infer causal relationships.
· Excellent verbal and written communication skills with the ability to effectively advocate technical solutions to research scientists, engineering teams and business audiences
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