Data Science Manager, Music Recommendations

• Act as a key partner to leaders in Spotify’s Music Vertical, with a focus on the Editorial function globally and recommendations of music content.

• Manage a strong team of data scientists, aligning their expertise and professional growth with business needs via ongoing coaching and roadmap planning.

• Conduct research yourself and craft compelling data storytelling to influence large investments at Spotify in the field of music recommendations

• In particular, orient innovation at the intersection of human editorial and algorithmic recommendations in the age of AI. 

• Upskill the data science function in your area with agentic AI, and develop the next layer of self-serve access to insights for your stakeholders 

• Become a company-wide expert on music content and how it reaches listeners around the world.

• 7+ years of relevant experience performing data analysis or data science. In particular…

  • You are highly proficient in Python and SQL

  • You are well versed in AI tools for data science and insights, such as Claude Code

  • You have an expertise in building data visualizations and dashboards (e.g. Looker Studio, Tableau or other)

  • You have experience with A/B testing and statistical modeling

• Experience managing a team is preferred. The role is opened to first time managers who have a proven experience mentoring others, leading large collaborative projects and coordinating with senior leaders. 

• You are a confident communicator with acute design thinking, who can simplifie complex problems and  ground creative discussions in objective data. You have experience presenting to and influencing senior leadership.

• You have a degree in a quantitative field, such as Computer Science/Engineering, Mathematics, Statistics, Economics; advanced degree is désirable 

• You are passionate about music. Previous experience in creative industries is a preference, but not a hard requirement if you have a deep empathy for how the music art form is created and enjoyed

This role is based in New York, New York

• We offer you the flexibility to work where you work best! There will be some in person meetings, but still allows for flexibility to work from home
• We ask that you come in 2-3 times per week