Data Scientist

### Who you are
- 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.)
- Ph.D. in Statistics, Economics, or related quantitative field with strong technical implementation focus
- Experience contributing to end-to-end technical infrastructure for experimentation or modeling systems
- Deep understanding of causal inference methodologies and challenges
- Familiarity with modeling techniques used to estimate long-term effects from short-term data
- Track record of translating complex statistical models into production systems
- Problem-solving mentality with demonstrated curiosity and ability to ask clarifying questions
- Experience in consumer technology or subscription-based business models
- Ability to balance sophisticated modeling discussions with hands-on technical implementation
- Experience with containerization technologies and cloud infrastructure (AWS preferred)

### What the job involves
- 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