Data Scientist, Amazon

• 2+ years of building machine learning models or developing algorithms for business application experience
• Experience in experimental design, causal inference, and statistical analysis
• Experience with statistical power analysis, sampling design, and bias/variance tradeoffs
• Experience contributing to scalable, production-quality code
• Master's degree in Statistics, Economics, Mathematics, or related quantitative field
• Experience with data querying languages (e.g. SQL), scripting languages (e.g. Python), or statistical/mathematical software (e.g. Python, R, Stata, Matlab, etc.)

Desirable

• We are seeking a Data Scientist to own our causal inference infrastructure and drive sophisticated modeling that measures the incremental impact of business decisions
• This role requires deep expertise in advanced causal inference methodologies—including synthetic control methods, Synthetic Difference-in-Differences (SDID), and Bayesian approaches—to design rigorous experiments, estimate long-term customer behavior effects, and translate complex analytical results into clear business recommendations
• You will own the development and continuous improvement of these causal inference models while being responsible for machine learning operations at scale to ensure our organization makes data-driven decisions with confidence
• At Audible, you will have an opportunity to make the best of your skillsets to both develop advanced scientific solutions and drive critical customer and business impact
• You will play a key role to drive end-to-end solutions from understanding our business and business requirements, identifying opportunities from a large amount of historical data and engaging in research to solve the business problems
• You'll seek to create value for both stakeholders and customers and inform findings in a clear, actionable way to managers and senior leaders
• You will be at the heart of an agile and growing area at Audible
• Design and execute geo-level randomized experiments to measure incremental impact
• Apply statistical techniques to evaluate causal impact in quasi-experimental settings
• Ensure experiments are statistically valid by evaluating sampling strategies, statistical power, and potential sources of bias
• Develop models that estimate long-term effects from short-term experiments using machine learning
• Estimate how changes in customer behavior persist and decay over time
• Own and maintain the geo-testing codebase, including deployment and scalability
• Implement machine learning models at scale with focus on performance optimization
• Partner with stakeholders to ensure models align with real business dynamics
• Engage deeply with business problems through curiosity-driven questioning and brainstorming
• Translate experimental results into financial impact and investment recommendations
• Analyze marginal and average revenue impacts relative to costs
• Communicate complex quantitative ideas clearly to non-technical stakeholders
• Demonstrate understanding of Audible's business model and customer experience