Amazon Data Scientist SAMBA 

United States, New Jersey, Newark 

939846828

ABOUT THIS ROLE
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 impacts. 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 cutting-edge 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.
As a Data Scientist, you will...- Perform content evaluation, apply natural language processing, analyze attributes and representations (in text, audio, cover art), generate content recommendations, and identify trends
- Conduct product-related analyses including user click stream analysis, search engine optimization, and product recommendations
- Evaluate marketing performance across earned, paid, and owned media evaluationABOUT AUDIBLE

- Currently pursuing a MS/PhD in computer science, statistics, mathematics, operations research, data science, economics, or another discipline involving experimental design and quantitative analysis of experimental data
- 1+ years of hands-on experience with machine learning, predictive modeling, and/or statistical analysis techniques
- Development experience with Python or equivalent scripting languages
- Experience in extracting data using SQL

- Exposure to software engineering environments (version control, command line)
- Machine Learning Pipeline orchestration with AWS (SageMaker, Batch, Lambda, Step Functions) or similar cloud-platforms
- Experience with causal inference methodologies for quasi-experimental settings (Synthetic Control, Difference-in-Difference, Regression Discontinuity, Propensity weighting)
- Experience in forecasting methodologies
- Ability to tackle very loosely defined problems and consistently deliver elegant, modular and scalable solutions in a timely manner