Data Scientist - Subscriptions

New
New York City, United StatesFull-TimeMiddle
Salary not disclosed
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Job Details

Experience
4+ years
Required Skills
PythonSQLArtificial Intelligence

Requirements

  • 4+ years of experience in data science or analytics, working with product or business teams.
  • Strong foundation in statistics, economics, mathematics, computer science, or a similar field, or equivalent practical experience.
  • Confident working with large datasets and strong skills in SQL and Python.
  • Experience designing and analyzing experiments and understanding how to measure impact with rigor.
  • Comfortable owning projects end-to-end, from framing the problem to delivering actionable insights.
  • Experience applying AI or automation in analytics workflows by integrating LLM’s to streamline multi-step analyses.
  • Communicate complex ideas in a clear, structured and engaging way.
  • Curious, proactive, and comfortable working in a fast-moving environment with some ambiguity.
  • Enjoy collaborating across teams and building strong working relationships.
  • Experience in consumer products, subscriptions, or monetization-focused environments.

Responsibilities

  • Explore how listeners experience Spotify Premium and identify opportunities to improve conversion, retention, and engagement.
  • Design and analyze experiments to understand the impact of new features and product changes.
  • Build and orchestrate AI-powered analytical workflows to automate complex analyses and scale output.
  • Define and track meaningful metrics that reflect the health and value of the Premium experience.
  • Develop dashboards and tools that help teams monitor performance and make informed decisions.
  • Investigate user behavior trends and bring forward insights that shape product direction.
  • Partner closely with cross-functional teams to align on goals and translate data into action.
  • Communicate insights clearly to a wide range of audiences, including senior stakeholders.
  • Contribute to a strong, collaborative data culture by sharing knowledge and supporting peers.
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