Data Science Manager, Amazon Music - DISCO
Amazon
Description
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.
Key job responsibilities
* A Data Science Manager serving this role will act as thought partner to L8s of Product and Engineering organizations
* Manage team of BIEs and Data Scientists leading strategic growth areas: retention, voice + visual engagement, organization-wide experimentation, Gen AI initiatives for customer sentiment analysis, and product excellence.
* Bridge science, analytics, and engineering to deliver solutions informing strategic decisions on AM technology investments and long-term business performance
* Lead experimentation program evolution, including culture enhancement, measurement efficiency tooling, and ROI tracking for AM leadership
* Build and maintain customer segmentation models to understand streaming behaviors and forecast responses to personalization changes
* Develop structural and predictive models using data science workflows to deliver measurable customer results
A day in the life
* Manage and develop data scientists specializing in experimentation and customer analytics
* Lead strategic relationships across music voice + visual, retention, experimentation, Gen AI, and FPE initiatives
* Conduct experimentation and hypothesis testing, build launch measurements, goal benchmarking, and forecasting
* Develop science models for targeting and create operational reporting with anomaly detection for product health monitoring
* Partner with product managers to translate business questions into actionable science projects
* Influence product roadmaps through data-driven insights on customer behavior patterns
* Provide hands-on leadership through daily stand-ups and sprint planning
* Review code, data warehouse designs, and oversee ETL pipeline development
* Monitor system performance and data quality metrics
* Conduct stakeholder meetings and present insights to senior leadership
* Design, build, and maintain real-time metrics, reports, analyses, and dashboards with full audit capabilities
* Build interactive dashboards and reports using Amazon QuickSight
* Identify and resolve customer experience friction points with cross-functional teams
* Establish performance metrics and audit mechanisms to track team progress
* Drive adoption of best practices and innovative solutions across the organization
About the team
The Data, Insights, Science and Optimization, Music Product & Technology (DISCO MPT) team is looking for a Manager Data Science to lead a team of Data Scientists, and Business Intelligence Engineers who analyze big data, provide analytics and insights as well as build models and build critical tools for experimentation. The DISCO MPT team enables the Music Product Technology organization to make data driven decisions that improve customer retention, engagement and experience on Amazon Music. We build and maintain automated self-service data solutions, data science models and deep dive difficult questions that provide actionable insights. We also enable measurement, personalization and experimentation by operating key data programs ranging from attribution pipelines to causal frameworks. In this role, you will set the vision and direction for the team and collaborate with internal stakeholders across marketing, growth, product, science and finance teams to scale and advance our science and analytics offerings.