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Salary
$95k – $192k per year (Estimated)
Location
Remote (United States)
Seniority
Middle · 4+ years exp
Employment
Full-Time
Overview
Company
Impact
Profile match
Spotify is an audio streaming platform that delivers an extensive catalog of music, podcasts, and audiobooks, catering to hundreds of millions of users globally. It empowers artists by providing them with the means to monetize their creativity while offering fans unparalleled access to a diverse array of content, all driven by a passion for sound.

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.
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