Data Scientist

Who you are

• 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

What the job involves

• 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
• 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
• 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
• 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
• 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
• Work across marketing, product, and engineering teams to frame key business questions and build credible analytical solutions
• Track emerging methods and apply them to improve measurement; maintain reproducible documentation and clear reporting for non-technical audiences