Senior Applied Scientist, Worldwide Returns & Recommerce - Science
Job Description
Welcome to Amazon's Worldwide Returns & ReCommerce (WWR&R) TeamAt WWR&R, we're revolutionizing returns management through our innovative "Zero Initiative," focusing on eliminating return costs, waste, and defects. Our mission extends beyond conventional business metrics to create lasting value for our customers, company, and environment.As pioneers in Amazon's circular economy, we're transforming how returns are handled through edge technology and operational excellence. Our approach combines sophisticated machine learning, automated routing systems, and innovative reuse channels to create seamless experiences for our customers while significantly reducing environmental impact.Our diverse team of experts in business, technology, and operations works collaboratively to manage the complete lifecycle of returned and damaged products. We're developing next-generation solutions that streamline the returns process, enhance product support, and create sustainable reuse opportunities.Join us in building scalable, high-impact solutions that shape the future of sustainable commerce while delivering exceptional customer experiences. At WWR&R, you'll be part of an innovative team that's committed to transforming returns management while contributing to a more sustainable future.Key job responsibilities-Design, develop, and evaluate highly innovative models for Natural Language Programming (NLP), Large Language Model (LLM), or Large Computer Vision projects.-Use SQL to query and analyze the data.-Use Python, Jupyter notebook, and Pytorch to train/test/deploy ML models. -Use machine learning and analytical techniques to create scalable solutions for business problems.-Research and implement novel machine learning and statistical approaches.-Mentor junior scientists and interns by providing technical guidance for their projects.-Work closely with data & software engineering teams to build model implementations and integrate successful models and algorithms in production systems at very large scale.About the teamWhen a customer returns a package to Amazon, the request and package will be passed through our WWRR machine learning (ML) systems so that we could improve the customer experience, identify return root cause, optimize re-use, and evaluate the returned package. Our problems touch multiple modalities spanning from: textual, categorical, image, to speech data. We operate at large scale and rely on state-of-the-art modeling techniques to power our ML models
Locations
- India, TS, Hyderabad, Hyderabad, TS, India
Salary
Salary not disclosed
Estimated Salary Rangemedium confidence
120,000 - 200,000 USD / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- - PhD, or Master's degree and 6+ years of applied research experienceintermediate
- - Experience programming in Java, C++, Python or related languageintermediate
- - Experience with neural deep learning methods and machine learningintermediate
- - Experience with popular deep learning frameworks such as MxNet and Tensor Flow.intermediate
Required Qualifications
- - PhD, or Master's degree and 6+ years of applied research experience (experience, 6 years)
- - Experience programming in Java, C++, Python or related language (experience)
- - Experience with neural deep learning methods and machine learning (experience)
- - Experience with popular deep learning frameworks such as MxNet and Tensor Flow. (experience)
Preferred Qualifications
- - Experience in building machine learning models for business application (experience)
- - Experience in building speech recognition, machine translation and natural language processing systems (e.g., commercial speech products or government speech projects) (experience)
- - PhD in math/statistics/engineering or other equivalent quantitative discipline, or Master's degree (degree in math)
- - Experience in building Computer Vision (CV) systems. (experience)
Responsibilities
- -Design, develop, and evaluate highly innovative models for Natural Language Programming (NLP), Large Language Model (LLM), or Large Computer Vision projects.
- -Use SQL to query and analyze the data.
- -Use Python, Jupyter notebook, and Pytorch to train/test/deploy ML models.
- -Use machine learning and analytical techniques to create scalable solutions for business problems.
- -Research and implement novel machine learning and statistical approaches.
- -Mentor junior scientists and interns by providing technical guidance for their projects.
- -Work closely with data & software engineering teams to build model implementations and integrate successful models and algorithms in production systems at very large scale.
- About the team
- When a customer returns a package to Amazon, the request and package will be passed through our WWRR machine learning (ML) systems so that we could improve the customer experience, identify return root cause, optimize re-use, and evaluate the returned package. Our problems touch multiple modalities spanning from: textual, categorical, image, to speech data.
- We operate at large scale and rely on state-of-the-art modeling techniques to power our ML models
Documents
Tags & Categories
Senior Applied Scientist, Worldwide Returns & Recommerce - Science
Job Description
Welcome to Amazon's Worldwide Returns & ReCommerce (WWR&R) TeamAt WWR&R, we're revolutionizing returns management through our innovative "Zero Initiative," focusing on eliminating return costs, waste, and defects. Our mission extends beyond conventional business metrics to create lasting value for our customers, company, and environment.As pioneers in Amazon's circular economy, we're transforming how returns are handled through edge technology and operational excellence. Our approach combines sophisticated machine learning, automated routing systems, and innovative reuse channels to create seamless experiences for our customers while significantly reducing environmental impact.Our diverse team of experts in business, technology, and operations works collaboratively to manage the complete lifecycle of returned and damaged products. We're developing next-generation solutions that streamline the returns process, enhance product support, and create sustainable reuse opportunities.Join us in building scalable, high-impact solutions that shape the future of sustainable commerce while delivering exceptional customer experiences. At WWR&R, you'll be part of an innovative team that's committed to transforming returns management while contributing to a more sustainable future.Key job responsibilities-Design, develop, and evaluate highly innovative models for Natural Language Programming (NLP), Large Language Model (LLM), or Large Computer Vision projects.-Use SQL to query and analyze the data.-Use Python, Jupyter notebook, and Pytorch to train/test/deploy ML models. -Use machine learning and analytical techniques to create scalable solutions for business problems.-Research and implement novel machine learning and statistical approaches.-Mentor junior scientists and interns by providing technical guidance for their projects.-Work closely with data & software engineering teams to build model implementations and integrate successful models and algorithms in production systems at very large scale.About the teamWhen a customer returns a package to Amazon, the request and package will be passed through our WWRR machine learning (ML) systems so that we could improve the customer experience, identify return root cause, optimize re-use, and evaluate the returned package. Our problems touch multiple modalities spanning from: textual, categorical, image, to speech data. We operate at large scale and rely on state-of-the-art modeling techniques to power our ML models
Locations
- India, TS, Hyderabad, Hyderabad, TS, India
Salary
Salary not disclosed
Estimated Salary Rangemedium confidence
120,000 - 200,000 USD / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- - PhD, or Master's degree and 6+ years of applied research experienceintermediate
- - Experience programming in Java, C++, Python or related languageintermediate
- - Experience with neural deep learning methods and machine learningintermediate
- - Experience with popular deep learning frameworks such as MxNet and Tensor Flow.intermediate
Required Qualifications
- - PhD, or Master's degree and 6+ years of applied research experience (experience, 6 years)
- - Experience programming in Java, C++, Python or related language (experience)
- - Experience with neural deep learning methods and machine learning (experience)
- - Experience with popular deep learning frameworks such as MxNet and Tensor Flow. (experience)
Preferred Qualifications
- - Experience in building machine learning models for business application (experience)
- - Experience in building speech recognition, machine translation and natural language processing systems (e.g., commercial speech products or government speech projects) (experience)
- - PhD in math/statistics/engineering or other equivalent quantitative discipline, or Master's degree (degree in math)
- - Experience in building Computer Vision (CV) systems. (experience)
Responsibilities
- -Design, develop, and evaluate highly innovative models for Natural Language Programming (NLP), Large Language Model (LLM), or Large Computer Vision projects.
- -Use SQL to query and analyze the data.
- -Use Python, Jupyter notebook, and Pytorch to train/test/deploy ML models.
- -Use machine learning and analytical techniques to create scalable solutions for business problems.
- -Research and implement novel machine learning and statistical approaches.
- -Mentor junior scientists and interns by providing technical guidance for their projects.
- -Work closely with data & software engineering teams to build model implementations and integrate successful models and algorithms in production systems at very large scale.
- About the team
- When a customer returns a package to Amazon, the request and package will be passed through our WWRR machine learning (ML) systems so that we could improve the customer experience, identify return root cause, optimize re-use, and evaluate the returned package. Our problems touch multiple modalities spanning from: textual, categorical, image, to speech data.
- We operate at large scale and rely on state-of-the-art modeling techniques to power our ML models