Amazon Data Scientist II 

United States, Washington, Seattle 

589038596

Key job responsibilities
• Implement statistical methods to solve specific business problems utilizing code (Python, R, Scala, etc.).
• Drive design and development of user classification models and other predictive models to enable a personalized experience for a user.
• Improve upon existing methodologies by developing new data sources, testing model enhancements, and fine-tuning model parameters.
• Collaborate with program management, product management, software developers, data engineering, and business leaders to provide science support, and communicate feedback; develop, test and deploy a wide range of statistical, econometric, and machine learning models.
• Build customer-facing reporting tools to provide insights and metrics which track model performance and explain variance.
• Communicate verbally and in writing to business customers with various levels of technical knowledge, educating them about our solutions, as well as sharing insights and recommendations.
• Earn the trust of your customers by continuing to constantly obsess over their needs and helping them solve their problems by leveraging technology
Seattle, WA, USASeattle, WA, USA

- Master's degree in a quantitative field, or Bachelor's degree and 5+ years of a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science experience
- 2+ years of data scientist experience involving data extraction, analysis, statistical modeling
- 3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
- 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
- Experience applying theoretical models in an applied environment

- Master's degree in a quantitative field
- Knowledge of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc.