Amazon.com Services LLC
Data Science, Applied Science, worldwide grocery stores
AppliedScientist,Grocery,Retail&InStoreExperience(GRAISE)
“Applied Scientist, Grocery, Retail & In-Store Experience (GRAISE) at Amazon.com Services LLC. Skills: Machine Learning, Computer Vision, Deep Learning. Design machine learning models. Implement machine learning models”
What You'll Achieve.
Improve shopping experience
Industry & Context.
Solve complex problems; Solve real-world problems
What They're Looking For.
Must Have
Master's degree and 2+ years experience, 2+ years building ML models, Experience in algorithms and data structures, Experience in parsing, Experience in numerical optimization, Experience in data mining, Experience in parallel and distributed computing, Experience in high-performance computing, Experience with MxNet, Experience with TensorFlow, Experience in deep learning models architecture design, Experience in deep learning training and optimization, Experience in model pruning
Nice to Have
PhD preferred, Experience in patents, Experience in publications, Experience with Python, Experience with Java, Experience with C++
What You'll Do.
Design machine learning models
Implement machine learning models
Conduct exploratory data analysis
Develop deep understanding of data challenges
Collaborate with software engineers
Ensure reliability at scale
Evaluate model performance
Evaluate business impact
Communicate recommendations
Stay current with research
Evaluate applicability to problems
Contribute to culture of scientific rigor
Contribute to culture of experimentation
Contribute to culture of continuous improvement
Analyze model performance
Deploy computer vision models
Prototype new approaches
Present findings to stakeholders
Translate technical results
Balance scientific thinking
Balance engineering constraints
How You'll Work.
Team & Collaboration
Engineering teams; Product teams; Business teams; Software engineers; Product stakeholders; Business stakeholders
Communication Scope
Communicate findings; Communicate recommendations
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