Content and Distribution Analytics Manager
Spotify
At Spotify, we’re focused on providing the right music for every moment. We’ve connected tens of millions of people to their favorite songs and created a service that people use to discover and share music they love. We’re currently in 55+ markets and growing fast!
As the Content & Distribution Analytics Manager, you will be part of Spotify’s Analytics team and be responsible for guiding Spotify’s content decisions by crunching large data sets, analyzing the results, and gaining insight. The Content & Distribution organization consists of Label Relations, Artist Services, Licensing and Content Operations.
Responsibilities:
Understand the impact of content on Spotify, as well as the impact of Spotify on content providers, through both internal and external data analysis
Defining how these findings should influence the strategy and tactic of Content & Distribution
Lead and develop a global analytics team; on a day-to-day basis, you will be leading the team, while also collaborating on multiple projects
- You will work very closely with the leadership of Content & Distribution, as well as peers on on the broader Analytics team (Product Analytics, Tech Analytics, Marketing Analytic and, Ad sales Analytics)
Requirements:
3-5 years of experience working with analysis; posess domain expertise in the music industry
Strong technical competence to perform advanced analytics: Coding skills (such as python, java, C) and experience performing analysis with large datasets, analytics tools experience (SPSS, R, excel, SQL, HIVE, Hadoop).
Demonstrated intellectual curiosity, logical thinking, and the ability to solve complex problems that may not always be well-defined
Demonstrated ability to condense analysis into actionable insights to inform business decisions and influence / transform business performance; understand business models and high level strategy.
- Possess statistical competence; demonstrated experience with hands-on statistical modeling (such as regression modeling, a/b testing, significance testing etc), knowledge about machine learning (such as predictive modeling, decision trees, classification models, clustering techniques), knowledge about statistical research methods (such as conjoint survey models, segmentation survey methods, etc)
Full Time