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Job Field: Environmental Jobs
Location: SEATTLE, WA
Salary: $-
Job Type: Part Time

JOB SUMMARY:

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               <td align="left"><font style="font-size: 100%;">Research Scientist/Predictive Modeler - Sales Technology<BR><BR><BR><BR>Job ID: 138908<BR>Location: US-WA-Seattle<BR><BR>Posted Date: 9/20/2012<BR>Company: Amazon Corporate LLC<BR><BR>Position Category: Research Science <BR>Apply for This Position<BR><BR>* Apply for this position online<BR>* E-mail this position to a friend<BR>Position Details<BR>Job Description<BR>Are you ready to make a BIG difference?<BR>Do you want to join a fast paced, innovative team who use cutting edge analytical tools and techniques to help grow Amazon?s 3P Seller business? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-art algorithms to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you like to innovate and simplify? If yes, then you may be a great fit to join Amazon Sales Technology team - the core system that enables us to recruit and develop millions of Sellers on Amazon marketplaces worldwide.<BR><BR>Learn more about our business athttp://www.amazonservices.com/<BR><BR>Sales of products by third-party sellers on our websites represent more than one-third of units sold, and the Seller Services business is growing faster than our Retail business. As an organization, Amazon?s North American Seller Services is uniquely highly influential by coordinating across Amazon Customers, Amazon Category teams and Amazon Sellers. We are a business development organization; we drive growth for over 2MM Amazon Sellers through business intelligence, cross-selling efforts and integrated Account Management. We are successful by focusing on aggressive growth for our Sellers? businesses.<BR><BR>Major responsibilities<BR><BR>* Develop targeting and segmentation strategies to drive profitable growth of Amazon?s Seller base.<BR>* Analyzing and understanding large amounts of Amazon?s historical business data for specific instances of Seller growth opportunities<BR>* Design, development and evaluation of highly innovative models for Seller targeting and development<BR>* Working closely with software engineering teams to drive real-time model implementations and new feature creations<BR>* Working closely with operations staff to optimize lead generation, recruitment and development of Sellers<BR>* Communicate, persuade and influence senior management through data driven analysis<BR>* Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation<BR>* Tracking general business activity and providing clear, compelling management reporting on a regular basis<BR>* Research and implement novel machine learning and statistical approaches<BR>Basic Qualifications<BR><BR>* A MS in Statistics, CS machine learning, Operational research or in a highly quantitative field / An MBA with proven quantitative orientation<BR>* 3+ years of hands-on experience in predictive modeling and analysis<BR>* 3+ years SAS or Matlab experience<BR>* 3+ years SQL experience<BR>Preferred Qualifications<BR><BR>* A PhD in Statistics                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             </font></td>
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