Overview

Spotify is looking for impressive technologists to join our data science practice and help drive the future of our promotions platform. You will be joining other extremely-talented engineers that share a common interest in solving problems with software and data. You will tackle complex data-related problems leveraging some of the most diverse datasets available — user behaviors, acoustical analysis, cultural and contextual data, and signal across our broad range of mobile and connected platforms. And you will work with a team of other world-class engineers to bring your solutions to the infrastructure and to our customers.

You will not only work on the world’s leading online music service, but you’ll also make a direct and tangible impact on the business by building the future of our music promotion and advertising capabilities. We are driven by the core idea that we can help brands to participate in the Spotify experience in a way that improves it for our end users. The technical complexity involved in truly delivering on that mission should be something you are excited to tackle.

Spotify’s culture puts the power in the hands of the individual, and you should thrive in this type of environment. You will touch production, impact revenue, and help grow our amazing team every single day.

Requirements:

You are fluent in multiple programming languages (dynamic or static typed)

You have a deep background in applying multiple disciplines in the data science domain to practical commercial problems

You are fluent processing and analyzing large data sets using a variety of production-class tools

You have experience with Linux/Unix systems

You have worked with distributed/scalable systems and high-volume applications

You care, and are passionate about what you do

Responsibilities:

Continuously design, develop, and test data-driven solutions to practical problems both well-defined and open-ended

Help drive the optimization and analysis agenda of the core promotional and advertising platform

Develop measurement approaches and leverage these to evaluate performance continuously

Leverage a wide array of data processing infrastructure technologies (batch and real-time) in your work

Span languages as needed, depending on the data processing framework in use

Collaborative with software engineers, ML experts, and others, taking learning and leadership opportunities that will arise every single day

Work as part of highly-dynamic cross-functional agile teams in a variety of lightweight processes to drive and deliver on new product objectives