Senior Data Scientist, Artist-First AI Music lab

Spotify•Boston, MA

1d•Hybrid

About The Position

The Senior Data Scientist will join Spotify’s Artist-First AI Music lab, part of the Music Mission team, which builds tools and services for music creators and fan experiences. The lab explores generative music technologies and develops experiences that center and celebrate artists and creatives. Its principles include working through upfront partnerships with labels, distributors, and publishers; allowing artists and rightsholders to choose whether and how to participate; creating new revenue streams with fair compensation and transparent credit; and using AI to help artists connect creatively with fans rather than replace human artistry. This role leads measurement and insights for generative music experiences. It develops frameworks and scalable metrics to understand how creators, listeners, and rights holders engage with generative AI music, and uses analyses of Spotify’s behavioral datasets, annotations, and experimentation platforms to evaluate systems and guide product and model development. The Senior Data Scientist synthesizes model, user behavior, and ecosystem evidence into strategic recommendations and narratives, and builds datasets, dashboards, and durable measurement systems to assess product health and impact. The role works closely with engineering, AI research, product, design, research, legal, trust and safety, marketing, policy, and licensing teams. The position offers flexibility in work location, with some in-person meetings, and team collaboration operates within the US Eastern timezone.

Requirements

Track record of using insights to shape strategy for major company initiatives involving many partners.
Significant hands-on experience in advanced analytics or data science; experience involving LLM evaluation, user-generated content, or complex user behavior is preferred.
Clear communication skills and ability to develop evidence-backed viewpoints and synthesize quantitative, qualitative, and model telemetry signals into narratives for senior executives, researchers, engineers, and the music industry.
Proficiency in Python and SQL, with experience analyzing large datasets.
Experience developing metrics frameworks for complex products to measure impact and support prioritization.
Experience with machine learning frameworks and designing and assessing multimodal or embedding-based models, including validation against LLM judges or human ground truth and evaluation beyond offline accuracy.
Solid grounding in experimental design and causal inference, including A/B tests, quasi-experiments, and power analysis.
Ability to tackle loosely defined problems in ambiguous, zero-to-one environments.

Nice To Haves

Experience in a domain involving LLM evaluation, user-generated content, or complex user behavior.
Use of AI productivity tools such as Cursor or Copilot.

Responsibilities

Lead foundational measurement and insights work for generative music experiences, creating frameworks to understand creator, listener, and rights-holder engagement.
Design and conduct large-scale analyses and evaluations of generative music systems, including prompt interpretation and model output quality, using behavioral datasets, annotations, and experimentation platforms.
Partner with engineers and AI researchers to integrate evaluation signals into model development, debugging, and product iteration.
Identify behavioral and market dynamics affecting sustainable consumer demand and creator participation, and translate findings into strategic recommendations.
Build practical, scalable metrics and decision systems for evaluating credibility, user delight, safety, creator alignment, and ecosystem trust in AI music.
Combine model, user behavior, and ecosystem signals into clear narratives that inform product strategy, cross-functional alignment, and long-term priorities.
Create datasets, dashboards, and long-term measurement systems for understanding product health and impact.
Collaborate with Product, Design, Research, Legal, Trust & Safety, Marketing, Policy, and Licensing teams, applying statistical rigor and principled measurement.