Data Scientist - Personalization

Key Skills

Java Programming, Python Programming, SQL, Big Data Technology, Machine learning techniques, Data Mining, Deep Learning, Data Modeling, Natural Language Processing (NLP), Algorithms, Amazon EMR, SPARK Programming, Large scale data processing, AWS Glue

Job Description

Good storytelling starts with great listening. At Audible, that means each role and every project has our audience in mind. Because the same people who design, develop, and deploy our products also happen to use them. To us, that speaks volumes.

ABOUT THE ROLE

Hey – there’s a revolution going on! A revolution in digital entertainment. Every day millions of people around the world choose to be more thoughtfully entertained, informed and educated by listening to fascinating stories and all forms of fictional and non-fictional content. Want to shape the future of this innovation through statistics and machine learning? If so, this may be a great fit for you!

As Data Scientist - Personalization, you will make the best of your skillset in predictive modeling to 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 will inform findings in a clear, actionable way to managers and senior leaders.

ABOUT THE TEAM

The Audible 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 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.

We are looking for motivated, results-oriented Data Scientist with strong rigor and demonstrable skills in Machine Learning, Deep Learning, Natural Language Processing, data mining and/or large-scale distributed computation.

As a Data Scientist - Personalization, you will...

• Develop and validate models to optimize the Who, When, Where and How of all our interactions with customers.
• Work closely with other data scientists, ML experts, engineers as well as business across globe, and on cross-disciplinary efforts with other scientists within Amazon.
• Contribute to the growth of the Audible Data Science team by sharing your ideas, intellectual property and learning from others.

BASIC QUALIFICATIONS

• MS in one of the following disciplines: Computer Science, Statistics, Data Science, Economics, Applied Math, Operational Research or a related quantitative field.
• Fluent in SQL, Python.

PREFERRED QUALIFICATIONS

• Passion for data (and fearlessness in the face of a data tsunami), modeling, research design and cutting-edges algorithms.
• 3+ years experience or PhD 1+ year.
• Domain knowledge of comparable products (digital, retail).
• Big Data Engineering with Spark / AWS EMR & Glue.

ABOUT AUDIBLE

At Audible, we innovate and inspire through the power of voice. We're changing the narrative on storytelling. As a leading creator and provider of premium audio storytelling, we've redefined the ways people access, discover, and share stories. The stories we tell have the ability to transport and transform everyday moments into meaningful experiences and it's our people who make Audible's service possible. We're listeners, storytellers, and problem-solvers. Our perspectives and experiences power our ideas and come together in our mission to unleash the power of the spoken word. Audible offers a Hub+Home hybrid workplace model that gives employees flexibility between gathering in a common office space (work from hub) and remote work (work from home). Some teams will work mostly at hub, some mostly at home and others hybrid. For more information, please visit adbl.co/hybrid.