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We are pioneers in areas such as recommendation engines, product search, eCommerce fraud detection, and large-scale optimization of fulfillment center operations. Were looking for talented scientists capable of applying ML algorithms and cutting-edge deep learning (DL) and reinforcement learning approaches to areas such as drug discovery, customer segmentation, fraud prevention, capacity planning, predictive maintenance, pricing optimization, call center analytics, player pose estimation, event detection, and virtual assistant among others.
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Work closely with account teams, research scientist teams, and product engineering teams to drive model implementations and new solution. As an applied scientist, you are proficient in designing and developing advanced ML models to solve diverse challenges and opportunities.
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Applied Scientist II, Generative AI Innovation Center. Job ID: 2643203 | Amazon Development Center U.S., Inc. Hands on experience building models with deep learning frameworks like MXNet, Tensorflow, or PyTorch.
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Principal Applied Scientist, Generative AI Innovation Center. The Generative AI Innovation Center helps AWS customers accelerate the use of Generative AI to solve business and operational challenges and promote innovation in their organization.
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Job ID: 2641363 | Amazon Development Center U.S., Inc. Experience with neural deep learning methods and machine learning. Experience in patents or publications at top-tier peer-reviewed conferences or journals.
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The Global Customer Fulfillment (GCF) Amazon Customer Excellence System (ACES) team is seeking a Data Scientist to define and model volume availability from a Fulfillment Center perspective, as in what is the ideal assignment curve to feed FCs in order to optimize network throughput, labor, transportation, speed, and quality (DEA.
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Amazon Robotics automates fulfillment center operations using various methods of robotic technology including autonomous mobile robots, sophisticated control software, language perception, power management, computer vision, depth sensing, machine learning, object recognition, and semantic understanding of commands.
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Our primary purpose is to transform and influence shipment flows from order to customer doorstep by ensuring that global and local planning engines have the right granularity of data to allow Fulfillment Centers to optimize performance while adhering to the networks flow plan while minimizing risk.
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We are looking for a motivated individual with strong analytic and communication skills to join the effort in evolving the fulfillment center network of tomorrow. At Amazon Worldwide Fulfillment Design and Engineering, we are designing the future and if you are in quest of an iterative fast-paced environment, where you can drive innovation through data visualization products, advance analytics and machine learning at scale, this is your opportunity.
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Work closely with account teams, research scientist teams, and product engineering teams to drive model implementations and new solutionAbout the teamAbout the teamDiverse ExperiencesAWS values diverse experiences.
$136,000Full-timeExpandApply NowActive JobUpdated Today
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How to Increase Job Ad Exposure
In today's competitive job market, writing quality job ads is critical for attracting top talent to your organization. While networking and candidate referrals are prime real estate for finding qualified candidates, nothing beats the tried-and-true method of writing an extraordinary job ad. But while writing a great job ad is the first step, what's more important is increasing visibility. You could have the most detailed, well-written ad on the internet, but if no one sees it, then you are wasting time (and potentially money!). Employers often believe that job boards are the root of the problem, but you can learn how to increase job ad exposure by tweaking a few steps of your recruitment process.