Senior Data Scientist, Artist-First AI Music lab

The Music Mission team owns Spotify's end to end proposition for music creators and the experiences they create for fans. The team is dedicated to building tools and services to enable creation, promotion, expression, and monetization at scale.

We're looking for a Senior Data Scientist to join the Artist-First AI Music lab. Our team explores and advances generative technologies for music to create new experiences for fans and artists, with a focus on listening experiences that center and celebrate artists and creatives. All of our products will put artists and songwriters first, through these four principles:

Partnerships with record labels, distributors, and music publishers:
We'll develop new products for artists and fans through upfront agreements, not by asking for forgiveness later.

Choice in participation:
We recognize there's a wide range of views on use of generative music tools within the artistic community. Therefore, artists and rights holders will choose if and how to participate to ensure the use of AI tools aligns with the values of the people behind the music.

Fair compensation and new revenue:
We will build products that create wholly new revenue streams for rights holders, artists, and songwriters, ensuring they are properly compensated for uses of their work and transparently credited for their contributions.

Artist-fan connection: AI tools we develop will not replace human artistry. They will give artists new ways to be creative and connect with fans. We will use our role as the place where more than 700 million people already come to listen to music every month to ensure that generative AI deepens artist-fan connections.

For more information, see this press release:

• Lead foundational measurement and insights work for generative music experiences, developing the frameworks that help Spotify understand how creators, listeners, and rights holders engage with generative AI music.
• Design and run large-scale analyses and evaluations of generative music systems, from prompt interpretation to model output quality, using Spotify's unique behavioral datasets, annotations, and experimentation platforms.
• Partner with Engineers and AI Researchers to integrate evaluation signals into model development loops, debugging workflows, and product iteration, making sure scientific rigor informs technical direction.
• Identify the behavioral and market dynamics that shape sustainable consumer demand and creator participation, and translate them into clear strategic recommendations.
• Build practical, scalable metrics and decision systems that define what "good" looks like for AI music: credibility, user delight, safety, creator alignment, and ecosystem trust.
• Bring together model, user behavior, and ecosystem signals into clear narratives that shape product strategy, cross-functional alignment, and long-term priorities across Spotify.
• Create the datasets, dashboards, and long-term measurement systems that give Spotify a lasting, comprehensive understanding of product health and impact.
• Work closely with Product, Design, Research, Legal, Trust & Safety, Marketing, Policy, and Licensing teams, bringing statistical rigor and principled measurement to help build artist-first systems that scale responsibly.

• You have a track record of using insights to shape strategy for major company initiatives involving many different partners.
• You have significant hands‑on experience in advanced analytics or data science, ideally in a domain involving LLM evaluation, user-generated content, or complex user behavior.
• You communicate clearly, form well‑founded, evidence‑backed points of view, and can bring together messy, multimodal signals (quantitative, qualitative, and model telemetry) into clear narratives for senior executives, researchers, engineers, and the music industry.
• You're proficient in advanced analytics with Python and SQL, have analyzed large datasets, and use AI tools (e.g. Cursor, Copilot) to increase your productivity and impact.
• You have experience developing metrics frameworks for complex products that help teams measure impact and reason clearly about priorities.
• You have experience with machine learning frameworks, designing and assessing multimodal or embedding‑based models, and validating them against LLM judges or human ground truth, with an understanding of how to evaluate models beyond offline accuracy.
• You have a solid grounding in experimental design and causal inference, such as A/B tests,…