Amazon
Job ID: 3091918 | Amazon.com Services LLC
Amazon Science gives you insight into the company’s approach to customer focused scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer‑centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Our scientists continue to publish, teach, and engage with the academic community, in addition to utilizing our working backwards method to enrich the way we live and work.
Please visit https://www.amazon.science for more information.
Are you an expert in Neural Network, Graph Models and Probabilistic Risk Modeling? Are you interested in building AI‑driven solutions on complex business problems that have significant global benefit? The Brand Protection ML team designs and builds high‑performance AI systems using machine learning that identify fraudulent and abusive entities to protect customers, brands, selling partners and the Amazon store worldwide from counterfeit and other forms of IP infringements.
We are looking for a highly talented scientist to help build our vision for Brand Protection. As an applied scientist on the team, you will play a key role in designing, developing, and deploying advanced graph‑based models that detect hidden relationships, anomalous behaviors, and emerging risks in large‑scale, complex data environments. You will work backwards from data insights and customer feedback to build the right machine learning solutions, and be resourceful in finding innovative solutions to unsolved problems. You will work closely with product team and engineering partners to launch the solution into production and own the end‑to‑end solution.
Major responsibilities
Understand business challenges by analyzing data and customer feedback
Collaborate with tech and product teams on building ML strategies, experimentation, implementation and continuous improvement post‑launch
Analyze and extract relevant information from large amounts of both structured and unstructured data to design strategies to solve business problems
Develop and apply graph‑based algorithms (e.g., Graph Neural Networks, representation learning, community detection, link prediction) for fraud and risk detection
Design scalable data pipelines to construct, enrich, and maintain large‑scale knowledge graphs from heterogeneous data sources
Present research and analytics insights with stakeholders and partners to advance organizational science knowledge and decision making
Research and implement novel AI solutions and publish research papers internally (and externally where appropriate)
About the team: Here at Selling Partner Services, we embrace our differences. We are committed to furthering our culture of inclusion. We have 14 employee‑led affinity groups, reaching 10,000+ employees in chapters globally. We have innovative benefit offerings, and we host annual and ongoing learning experiences, including our DEI Ambassador Program. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.
Basic Qualifications
PhD, or Master’s degree and 2+ years of building machine learning models for business application experience
Experience in state‑of‑the‑art deep learning models architecture design and deep learning training and optimization and model pruning
Experience programming in Java, C++, Python or related language
Experience implementing algorithms using both toolkits and self‑developed code
Preferred Qualifications
PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field
2+ years of solving business problems through machine learning, data mining and statistical algorithms experience
Experience with generative deep learning models applicable to the creation of synthetic humans like CNNs, GANs, VAEs and NF
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $136,000/year in our lowest geographic market up to $223,400/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job‑related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign‑on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.
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Amazon Science gives you insight into the company’s approach to customer focused scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer‑centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Our scientists continue to publish, teach, and engage with the academic community, in addition to utilizing our working backwards method to enrich the way we live and work.
Please visit https://www.amazon.science for more information.
Are you an expert in Neural Network, Graph Models and Probabilistic Risk Modeling? Are you interested in building AI‑driven solutions on complex business problems that have significant global benefit? The Brand Protection ML team designs and builds high‑performance AI systems using machine learning that identify fraudulent and abusive entities to protect customers, brands, selling partners and the Amazon store worldwide from counterfeit and other forms of IP infringements.
We are looking for a highly talented scientist to help build our vision for Brand Protection. As an applied scientist on the team, you will play a key role in designing, developing, and deploying advanced graph‑based models that detect hidden relationships, anomalous behaviors, and emerging risks in large‑scale, complex data environments. You will work backwards from data insights and customer feedback to build the right machine learning solutions, and be resourceful in finding innovative solutions to unsolved problems. You will work closely with product team and engineering partners to launch the solution into production and own the end‑to‑end solution.
Major responsibilities
Understand business challenges by analyzing data and customer feedback
Collaborate with tech and product teams on building ML strategies, experimentation, implementation and continuous improvement post‑launch
Analyze and extract relevant information from large amounts of both structured and unstructured data to design strategies to solve business problems
Develop and apply graph‑based algorithms (e.g., Graph Neural Networks, representation learning, community detection, link prediction) for fraud and risk detection
Design scalable data pipelines to construct, enrich, and maintain large‑scale knowledge graphs from heterogeneous data sources
Present research and analytics insights with stakeholders and partners to advance organizational science knowledge and decision making
Research and implement novel AI solutions and publish research papers internally (and externally where appropriate)
About the team: Here at Selling Partner Services, we embrace our differences. We are committed to furthering our culture of inclusion. We have 14 employee‑led affinity groups, reaching 10,000+ employees in chapters globally. We have innovative benefit offerings, and we host annual and ongoing learning experiences, including our DEI Ambassador Program. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.
Basic Qualifications
PhD, or Master’s degree and 2+ years of building machine learning models for business application experience
Experience in state‑of‑the‑art deep learning models architecture design and deep learning training and optimization and model pruning
Experience programming in Java, C++, Python or related language
Experience implementing algorithms using both toolkits and self‑developed code
Preferred Qualifications
PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field
2+ years of solving business problems through machine learning, data mining and statistical algorithms experience
Experience with generative deep learning models applicable to the creation of synthetic humans like CNNs, GANs, VAEs and NF
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $136,000/year in our lowest geographic market up to $223,400/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job‑related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign‑on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.
#J-18808-Ljbffr