Amazon Data Scientist II Amazon Privat Brands 

United States, New York, New York 

706687431

You will work with business leaders and economists to translate business and functional requirements into concrete deliverables, including the design, development, testing, and deployment of highly scalable distributed solutions. You will invent and implement scalable ML and econometric models while building tools to help our customers gain and apply insights. This is a unique, high visibility opportunity for someone who wants to have business impact, dive deep into large-scale science problems, enable measurable actions on the Consumer economy, and work closely with scientists and economists. If you are interested in Machine Learning, Generative AI, and large-scale intelligent solutions then this is the role you have been looking for.Key job responsibilities
* You will take the lead on large projects that span multiple teams. The problems you solve will be ambiguous, requiring both technical and domain expertise. You will deliver significant benefits to business with minimal assistance.* You will make solutions simpler. You will optimize connected systems using their dynamics. You will improve the consistency and integration between your team’s solutions and the work being done by related teams.
* You will improve the work done by others, either via a collaborative effort or by increasing their scientific knowledge, using specialized tools or advanced techniques. You will lead and actively participate in scientific reviews for your team and others.* You actively participate in the hiring process and improve the skills and knowledge of others via mentoring.

- 2+ years of data scientist experience
- 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