Data Scientist, Amazon

• Experience with data scripting languages (e.g., SQL, Python, R, or equivalent) or statistical/mathematical software (e.g., R, SAS, Matlab, or equivalent)
• Experience with machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance
• MS in one of the following disciplines: Computer Science, Statistics, Data Science, Economics, Applied Mathematics, Operational Research or a related quantitative field
• Experience in modeling, research design

Desirable

• In this role, you will build scalable solutions and models to support our business functions (Content, Marketing, Product)
• Leveraging a range of methods including machine learning and simulation, you will explain, quantify, predict and prescribe in support of informing critical business decisions
• You will translate business goals into agile, insightful analytics
• You will seek to create value for both stakeholders and customers and inform findings in a clear, actionable way to managers and senior leaders
• This position is for a 24 month contract
• Audible's Data Science team partners with marketing, content, product, and technology partners to solve business and technology problems using scientific approaches to build product and services that surprise and delight our customers
• We employ scalable cutting-edge Data Science (DS), machine learning (ML), deep learning (DL), and Natural Language Processing (NLP) knowledge to better target customers and prospects, understand and personalize the content, and context needed to optimize their book-listening experience
• We operate in an agile environment in which we own and collaborate on the life cycle of research, design, and model development of relevant projects
• As a Data Scientist, you will..
• Develop and validate models to optimize the Who, When, Where and How of all our interactions with customers
• Develop Audible-wide data engineering pipelines
• Imagine and invent before the business asks, and create groundbreaking applications using cutting-edge approaches
• Open the Science black-box, use causal inference and develop compelling data visualizations
• Work closely with other data scientists, ML experts, engineers and on cross-disciplinary efforts with other scientists within Amazon