Amazon Data Scientist Workforce Solutions - Talent Mobility 

United States, Washington, Seattle 

237478605

Key job responsibilities
• Implement statistical methods to solve specific business problems utilizing code (Python, R, Scala, etc.).
• Drive design and development of user classification/predictive models through NAWS based data pipelines 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 product management, software developers, data engineering, and business leaders to define product requirements, provide analytical 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
A day in the life
The data scientist will partner with Product, UX and Dev teams to own end-to-end business problems and metrics with a direct impact on employee experience. Success in this role will include delivering solutions with measurable business impact. The problems you will consider will be difficult to solve and often require a range of data science methodologies combined with subject matter expertise. You will need to be capable of gathering and using complex data set across domains. You will deliver artifacts on small to medium sized projects, define the methodology, and own the analysis. Your findings will affect important business decisions. Solutions are testable and reproducible. You will create documents and share findings in line with scientific best practices for both technical and nontechnical audiences.

- 1+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
- 2+ years of data/research scientist, statistician or quantitative analyst in an internet-based company with complex and big data sources experience

- Knowledge of statistical packages and business intelligence tools such as SPSS, SAS, S-PLUS, or R
- Experience with clustered data processing (e.g., Hadoop, Spark, Map-reduce, and Hive)