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 on their smartphones and other mobile devices.
Audible GmbH an Amazon company, is the leading provider of premium digital spoken audio information and entertainment on the internet, offering customers a new way to enhance and enrich their lives every day. Audible's content offering comprises more than 200,000 audiobooks and audio plays from 800 publishers. Titles downloaded from Audible are compatible with hundreds of mobile devices, including the iPhone and Android smartphones. Audible is also the preeminent provider of spoken-word audio products for Apple's iTunes® Store.
Based in Berlin, Audible GmbH is looking for a Working Student – Data Engineering (m/f).
As Working Student in our interdisciplinary research team of Data Scientists you will be responsible for modeling in many business areas, including but not limited to Customer: segmentation, care, acquisition, retention, engagement, propensity, and value; Product: simulation, testing, and evaluation; Content: acquisition, pricing, valuation, and recommendation.
You have a passion for Big Data and its supporting technologies and platform components. You will be an enabler for cutting edge modeling efforts, passionate about accessing, moving, and processing terabytes of data in a performant, scalable, fault-tolerant way, and will display your strong problem-solving ability under different settings (e.g. development, test, production, …)
You will be able to thrive in the powerful, advanced environment that makes Audible such a unique company in the area of data-processing technology, and will be offered a perfect mix of responsibility and mentoring, as well as ideal, flexible conditions to continue to study. You will also – if you wish - get exposure to modeling tasks further downstream of the Data Science value creation process.
· Pull large data sets from Oracle and Redshift database for ad-hoc analysis using SQL
· Process huge data sets using Spark, MapReduce, and Amazon Web Services
· Recognize patterns in the data and dig deep into anomalies