Amazon
Machine Learning Science, Applied Science, transportation and logistics
AppliedScientist,WorldwideReturns&Recommerce
“Applied Scientist, Worldwide Returns & Recommerce at Amazon. Skills: Machine Learning, NLP, LLM, Computer Vision. Design models for NLP. Develop models for NLP”
What You'll Achieve.
Achieve zero cost of returns; Achieve zero waste; Achieve zero defects; Create long-term value; Improve customer experience; Optimize re-use; Evaluate returned package
Industry & Context.
Optimize re-use; Identify return root cause; Solve complex business problems; Optimize business
What They're Looking For.
Must Have
2+ years building models for business application, Master's degree and 4+ years CS, CE, ML or related field experience, 2+ years experience programming in Java, C++, Python or related language, 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 Tensor Flow
Nice to Have
Experience building machine learning models or developing algorithms for business application, Experience in building speech recognition systems, Experience in building machine translation systems, Experience in building natural language processing systems, Experience developing and implementing deep learning algorithms, Experience with computer vision algorithms, Experience in patents or publications at top-tier peer-reviewed conferences or journals
What You'll Do.
Design models for NLP
Develop models for NLP
Evaluate models for NLP
Design models for LLM
Develop models for LLM
Evaluate models for LLM
Design models for Large Computer Vision Models
Develop models for Large Computer Vision Models
Evaluate models for Large Computer Vision Models
Analyze data using SQL
Train ML models using Python
Test ML models using Python
Deploy ML models using Python
Train ML models using Jupyter notebook
Test ML models using Jupyter notebook
Deploy ML models using Jupyter notebook
Train ML models using Pytorch
Test ML models using Pytorch
Deploy ML models using Pytorch
Create scalable solutions using machine learning
Create scalable solutions using analytical techniques
Implement novel machine learning approaches
Implement novel statistical approaches
Work with data engineering teams
Work with software engineering teams
Build model implementations
Integrate models in production systems
Integrate algorithms in production systems
How You'll Work.
Team & Collaboration
Data engineering teams; Software engineering teams
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