The Music Mission enables music creators to grow, engage, and monetize their fan bases on Spotify. Central to the Music Mission's vision is the development of promotional tools for artists and label teams, powered by Spotify's deep knowledge of listener behavior. Products like Discovery Mode, Marquee, Showcase, Music Videos, and Clips help artists and their teams grow their audiences, connect with fans, and achieve their goals on Spotify.
We're looking for a Data Scientist to join Discovery Mode within the Music Mission. Discovery Mode is a tool for artists and music marketers designed to help find new listeners when it matters most. With Discovery Mode, artists and labels identify songs that are a priority, and our systems use that signal to inform the algorithms that power personalized recommendations. This role sits within the ML squad that builds and operates the models behind Discovery Mode's measurement system, and you'll serve as the squad's analytical lead.
In this role, you'll partner closely with product managers and ML engineers to evaluate and improve the models that power Discovery Mode. You'll tackle complex analytical problems by designing experiments, developing evaluation frameworks, and building the analytical foundations that help keep our models accurate, reliable, and impactful for artists. As part of the Product Insights team within Music Mission, you'll help shape the measurement systems behind one of Spotify's most important promotion products.
What You'll Do
- Own the analytical function for the Discovery Mode ML squad, driving evaluation and continuous improvement of the models that power measurement and campaign optimization
- Partner with ML engineers to develop evaluation frameworks and identify opportunities to improve model performance, reliability, and customer impact
- Design and execute rigorous experiments to evaluate model quality, measure outcomes, and guide model development
- Conduct deep-dive analyses to assess model performance and translate findings into clear, actionable recommendations for product and business stakeholders
- Build, maintain, and evolve dashboards that track model health, customer metrics, and program performance
- Collaborate with product managers, engineers, and cross-functional partners to align analytical priorities with squad goals and customer needs
- Contribute to the broader Product Insights community by sharing best practices and helping raise the bar for analytics across Discovery Mode
Who You Are
- You have 4+ years of experience in a data science role and a degree in data science, statistics, economics, mathematics, or a related quantitative field
- You have experience measuring customer outcomes, defining KPIs, and connecting analytical insights to product decisions
- You know how to design and implement A/B tests, understand when experimentation is the right tool, and interpret results with appropriate rigor
- You have experience evaluating machine learning model performance and partnering with ML engineers to improve model and customer outcomes
- You are comfortable working in a highly technical environment and collaborating closely with engineering partners
- You communicate complex statistical concepts clearly to both technical and non-technical audiences
- You have strong data science fundamentals, including Python, SQL, BigQuery, dbt, data storytelling, and experience working within cross-functional product teams
- You have experience in areas such as advertising measurement, recommendation systems, experimentation, or causal inference at scale
Where You'll Be
- We offer you the flexibility to work where you work best! For this role, you can be within the EST timezone region as long as we have a work location.
- This team operates within the Eastern Standard time zone for collaboration.
Skills Required
- 4+ years of experience in a data science role
- Degree in data science, statistics, economics, mathematics, or a related quantitative field
- Experience measuring customer outcomes, defining KPIs, and connecting insights to product decisions
- Ability to design and implement A/B tests and interpret results rigorously
- Experience evaluating machine learning model performance and partnering with ML engineers to improve outcomes
- Strong data science fundamentals including Python, SQL, BigQuery, and dbt
- Experience with data storytelling and working within cross-functional product teams
- Experience in advertising measurement, recommendation systems, experimentation, or causal inference at scale
- Ability to communicate complex statistical concepts clearly to technical and non-technical audiences
Spotify Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Spotify and has not been reviewed or approved by Spotify.
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Flexible Benefits — Employees consistently praise the total compensation package beyond base salary, highlighting a mix of RSUs, cash incentives, and stipends alongside core pay. The package is described as flexible and customizable through equity choices (e.g., RSUs, options, cash) that can be tailored for long-term wealth building.
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Leave & Time Off Breadth — Time-off offerings are repeatedly highlighted as substantial, including generous vacation, paid sick days, volunteer time, and flexible holidays. These policies are framed as a meaningful part of the overall rewards experience beyond salary.
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Healthcare Strength — Health coverage is portrayed as comprehensive, spanning medical, dental, vision, life insurance, disability coverage, and mental health support. Additional employer contributions to HSAs are cited as strengthening the overall health and wellness value proposition.
Spotify Insights
What We Do
Spotify transformed music listening forever when it launched in Sweden in 2008. Discover, manage and share over 50m tracks for free, or upgrade to Spotify Premium to access exclusive features including offline mode, improved sound quality, and an ad-free music listening experience.

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