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.
We are seeking a highly skilled and analytical Data Scientist. You will play an integral part in the measurement and optimization of Amazon Music marketing activities. You will have the opportunity to work with a rich marketing dataset together with the marketing managers. This role will focus on developing and implementing models that aids in audience segmentation, AI-enablement in data/reporting, and assessing randomized controlled trials to rate marketing effectiveness. This role is suitable for candidates with strong background in cohort analysis, causal inference, statistical analysis, and data-driven problem-solving, with the ability to translate complex data into actionable insights. As a key member of our team, you will work closely with cross-functional partners to optimize marketing strategies and drive business growth.
Key job responsibilities
Develop Causal & Predictive Models
Build and validate causal and predictive models that quantify how marketing and lifecycle programs affect customer retention, engagement, and subscriber growth for Amazon Music. Own the team's models end to end.
Statistical Analysis at Scale
Analyze large customer datasets using SQL and Python or R to interpret results and uncover meaningful patterns, grounding your work in trusted, well-governed data.
Enable Data-Driven Decisions
Partner with marketing and finance stakeholders to deliver recommendations that improve retention and return on investment. Prioritize the work with the greatest impact on customer growth, and present findings clearly to both technical and executive audiences.
Bring AI into Self-Service Tools
Partner with product managers and engineers to incorporate AI and machine learning into the team's self-service analytics tools, so stakeholders can answer their own questions faster and at greater scale.
Cross-Functional Problem Solving
Work across marketing, product, and engineering teams to frame key business questions and build credible analytical solutions.
Innovate & Document
Track emerging methods and apply them to improve measurement; maintain reproducible documentation and clear reporting for non-technical audiences.
- Experience working as a Data Scientist
- Experience with data scripting languages (e.g., SQL, Python, R, or equivalent) or statistical/mathematical software (e.g., R, SAS, Matlab, or equivalent)
- Experience with machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance
- Experience applying theoretical models in an applied environment
- Experience in Python, Perl, or another scripting language
- Experience in a ML or data scientist role with a large technology company
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