We are looking for a Data Scientist, Media and Advertising Analytics to join Spotify’s Advertising Research and Measurement team. Our mission is to turn terabytes of data into insights, and get a deep understanding of the world of music and listeners. Together with us you will study user behavior, evaluate strategic initiatives and experiment with new features. Above all, your work will impact the way the world experiences music.
What you’ll do
- Work closely with cross-functional teams of analysts, designers and engineers who are passionate about Spotify’s success
- Analyzing the depth and breadth of Spotify’s user, music and podcast data to help drive thought leadership for both our advertising partners as well as publications for Brands @ Spotify
- Build data pipelines for first party Spotify data, staging data for analysis and reporting
- Developing Tableau dashboards for use by the research, measurement and insights teams
- Collaborating with data science, research and engineering teams across Spotify.
Who you are
- You know how to understand and solve loosely defined problems and come up with relevant answers and actionable insights
- You have 3-5 years of relevant experience, with a degree in economics, psychology, social science, statistics, mathematics or another quantitative discipline
- You have a passion for digital media and advertising and want to uncover new insights to guide the market
- You have the technical competence to perform advanced analytics: coding skills (Python primarily), experience with analytics tools (Pandas, SQL, Tableau / Tableau Server) and experience performing analysis with large datasets include hosted data architectures (BigQuery/Google Cloud)
- You have experience building data pipelines for reporting and analytics
- You are Tableau fluent and can drive production and management of Tableau dashboards for non-technical users
- You possess statistical competence (such as regression modeling, a/b testing, significance testing etc)
It is a plus if you
- have demonstrated experience with hands-on statistical modeling and possess knowledge about machine learning (such as predictive modeling, decision trees, classification models, clustering techniques)
We are proud to foster a workplace free from discrimination. We strongly believe that diversity of experience, perspectives, and background will lead to a better environment for our employees and a better product for our users and our creators. This is something we value deeply and we encourage everyone to come be a part of changing the way the world listens to music.