Spotify Careers London – Summer Internship in Brighton, UK

Overview

Position Title:Summer Internship

Company:Spotify

Location:Brighton, UK

Job Description:

The Personalization team makes deciding what to play next easier and more enjoyable for every listener. From Daily Mix to Discover Weekly, we’re behind some of Spotify’s most-loved features. We built them by understanding the world of music and podcasts better than anyone else. Join us and you’ll keep millions of users listening by making great recommendations to each and every one of them. We ask that our team members be physically located in Central European time or Eastern Standard/Daylight time zones for the purposes of our collaboration hours.

Responsibilities:

• You will apply your scientific knowledge to develop and evaluate machine learning methods, combining insights from statistics, signal processing, probabilistic modeling, reinforcement learning and many other fields.
• You will be a valued member of an autonomous, multi-functional team working in collaboration with other scientists, engineers, product managers, designers, user researchers, and analysts across Spotify to craft creative solutions to ambitious problems.
• You’ll help to develop intelligent, efficient and intuitive new ways to search, explore and discover music.

Requirements:

• You are pursuing a PhD Programme in Machine Learning, Computer Science, Data Science, Statistics or related areas with a strong computational focus.
• You have publications in communities such as WWW, SIGIR, WSDM, RecSys, KDD, AAAI, NeurIPS, ICML, ICLR, AISTATS, UAI, or related, in the following topics: recommendations, user understanding, large scale experimentation or (more broadly) machine learning, causal inference, reinforcement learning.
• You have strong coding skills in the following languages/libraries: Python, Numpy, TensorFlow, Scipy or Scikit-learn.
• You have experience in one or more of the following fields: Machine learning, Reinforcement Learning, Causal Inference, and Probabilistic Machine Learning.