Sr. Research or Applied Scientist (Berlin or Luxembourg City)
Amazon.com
Berlin, DE
vor 9 Tg.

At Amazon, we get our energy from inventing on behalf of customers. Success is measured against the possible, not the probable.

We are inventors that are working to build the best solutions to unique problems through breakthrough technology for example, Kindle has revolutionized the way customers consume digital content.

Working on the EU Amazon Content team will be unlike any job you have had.

The Kindle EU Content team is looking for an experienced Sr. Applied or Research Scientist based in the Luxembourg and / or Berlin Office.

This role requires an individual with excellent analytical abilities, deep knowledge of predictive analytics (supervised, unsupervised learning) and Optimization techniques.

The successful candidate should be able to learn by doing and apply methods quickly, iterating as necessary to drive business value.

We are looking for someone with big love for big data. You will help us solve complex business problems and challenges we are facing by using top-

of-the-line machine learning techniques and practices. This is a hands-on, high-impact technical position. You will spend most of your time writing code and wrangling data.

The successful candidate will also be a self-starter, be comfortable with ambiguity, have strong attention to detail, and will be comfortable accessing and working with data from multiple sources.

Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success.

We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build.

We offer a competitive salary and stock units, we offer a whole host of other benefits, including an employee discount and pension scheme.

We are committed to keeping compensation fair and equitable.

Basic qualifications

  • Doctorade degree in computer science, statistics, information systems, economics, mathematics or similar
  • 5+ years of experience working with large-scale, complex datasets to create / optimize machine learning, predictive, forecasting, and / or optimization models
  • Strong proficiency in SQL and R / Python / Scala is required
  • Very strong self-learning skills. Ability to pick up and adapt modeling methods from other disciplines or leverage methods from colleagues in other departments.
  • Practical understanding and hands-on experience with the following :
  • Supervised learning methods (linear and logistic regression, generalized linear models, decision trees, random forests, support vector machines, graphical models, neural networks / deep learning, etc.).
  • Unsupervised learning methods (K-means, hierarchical clustering, association rules, principal components, etc.).
  • Mathematical optimization (mixed integer programming, linear programming, stochastic programming / optimization discrete optimization convex optimization, reinforcement learning, etc.).
  • Preferred qualifications

  • PhD in a quantitative field such as Economics, Mathematics, Information Systems, Statistics, Operations Research or Computer Science
  • Experience in NLP / Deep Learning / Reinforcement Learning
  • Verbal / written communication & data presentation skills, including an ability to effectively communicate with both business and technical teams.
  • Understanding of our technology environment, and the performance and scalability issue associated with certain applications.
  • Superb attention to details and organizational skills
  • Step 2
    Bewerben
    Zu Favoriten hinzufügen
    Aus Favoriten entfernen
    Bewerben
    Meine Email
    Wenn Sie auf "Fortfahren" klicken, stimmen Sie zu, dass neuvoo Ihre persönliche Daten, die Sie in diesem Formular angegeben haben, sammelt und verarbeitet, um ein Neuvoo-Konto zu erstellen und Sie gemäß unserer Datenschutzerklärung per Email zu benachrichtigen. Sie können Ihre Zustimmung jederzeit widerrufen, indem Sie diesen Schritten folgen.
    Fortfahren
    Bewerbungsbogen