Data Scientist , Amazon Music - DISCO

Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. We offer experiences that serve all listeners with our different tiers of service: Prime members get access to all the music in shuffle mode, and top ad-free podcasts, included with their membership; customers can upgrade to Amazon Music Unlimited for unlimited, on-demand access to 100 million songs, including millions in HD, Ultra HD, and spatial audio; and anyone can listen for free by downloading the Amazon Music app or via Alexa-enabled devices. Join us for the opportunity to influence how Amazon Music engages fans, artists, and creators on a global scale.

The Data, Insights, Science and Optimization, Music Product and Tech (DISCO M&G) team is looking for a Data Scientist to join a team of scientists and engineers who analyze big data, provide analytics and insights and build models and algorithms to power Music product experiences. In this role, you will set the science vision and direction for the team and collaborate with internal stakeholders across marketing tech, Product , science and finance to scale and advance our science offerings. You will lead large scale science solutions, prioritize across multiple stakeholders and projects and be part of a fast-paced, dynamic and fun environment.


Key job responsibilities

• Lead the research and development of models and science products powering customer identity, acquisition, and retention strategies for Amazon Music

• Partner with Marketing and Growth leadership to develop science-driven business strategies, including subscriber segmentation, lifecycle management, and upsell programs

• Build predictive models — including propensity, uplift, and causal inference models — to support targeted marketing initiatives, measure incremental impact, and automate deep dives into key metric variances

• Collaborate with product, engineering, and marketing teams to evaluate the impact of growth campaigns, features, and algorithmic interventions through rigorous experimentation

• Analyze experiment results and provide data-driven recommendations to optimize subscriber acquisition and retention outcomes

• Partner with Senior Data Scientists and Product Managers to define, propose, and validate success and guardrail metrics for identity and growth programs

• Educate marketing and growth teams on analytics, measurement frameworks, and experimentation best practices, encouraging a culture of evidence-based decision making
About the team
The DISCO team focuses on accelerating Amazon Music customer growth by empowering product teams to make sound, customer-centric decisions through data and insights. We build data pipelines, self-service analytics, insights and predictive models enabling acquisition, engagement and retention at scale with personalized customer touch points.

Basic Qualifications

- 2+ years of data scientist experience
- 3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
- 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
- 1+ years of working with or evaluating AI systems experience
- Master's degree in Science, Technology, Engineering, or Mathematics (STEM), or experience working in Science, Technology, Engineering, or Mathematics (STEM)
- Experience applying theoretical models in an applied environment

Preferred Qualifications

- Ph.D. in Science, Technology, Engineering, or Mathematics (STEM)
- Knowledge of machine learning concepts and their application to reasoning and problem-solving
- Experience in Python, Perl, or another scripting language
- Experience in a ML or data scientist role with a large technology company
- Experience in defining and creating benchmarks for assessing GenAI model performance
- Experience working on multi-team, cross-disciplinary projects
- Experience applying quantitative analysis to solve business problems and making data-driven business decisions
- Experience effectively communicating complex concepts through written and verbal communication

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