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Amazon Sr Data Scientist Targeting Tooling 
United States, Georgia, Atlanta 
688085915

04.09.2024
DESCRIPTION

As a Sr. Data Scientist on the Targeting & Tooling team, you will have the opportunity to gain deep insights into customer needs and accelerate our go-to-market strategies. Leveraging cutting-edge AI and machine learning techniques, you will provide actionable and understandable recommendations that enable our sales teams to anticipate customer requirements and identify those best positioned to benefit from Amazon Web Services (AWS) cloud solutions. You will work closely with stakeholders throughout the organization to consume, analyze, and interpret customer data from a variety of sources.You will convey technical concepts clearly to both technical and non-technical audiences. You will identify trends and tell compelling data stories.
Key job responsibilities
- Leverage machine learning and advanced analytical techniques to extract meaningful insights from large, complex data sets related to GenAI, Enterprise Applications, Containerized Applications, and Serverless applications.
- Communicate analysis, results and recommendations clearly to key stakeholders to influence Go-To-Market strategy.- Develop best practices for building, evaluating, deploying and monitoring analytical models in production environments.A day in the life
You will translate business goals into focused, data-driven projects. This involves identifying leading indicators and unique data sources to model ideal customer profiles and high-potential targets.

BASIC QUALIFICATIONS

- 5+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
- 4+ years of data scientist experience
- Experience with statistical models e.g. multinomial logistic regression


PREFERRED QUALIFICATIONS

- 2+ years of data visualization using AWS QuickSight, Tableau, R Shiny, etc. experience
- Experience managing data pipelines
- Experience as a leader and mentor on a data science team