Amazon
Applied Scientist, Account Integrity
Amazon - San Diego, California, United States, 92101
Work at Amazon
Overview
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Overview
Account Integrity Team Opportunity
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by protecting Amazon customers from hackers and bad actors? Do you want to build advanced algorithmic systems that help manage the trust and safety of millions of customers every day? Are you excited by the prospect of analyzing and modeling terabytes of data and create state-of-the-art algorithms to solve real-world problems? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Amazon Account Integrity team. The Amazon Account Integrity team works to ensure that customers are protected from bad actors trying to access their accounts. Our greatest challenge is protecting customer trust without unjustly harming good customers. To strike the right balance, we invest in mechanisms which allow us to accurately identify and mitigate risk, and to quickly correct and learn from our mistakes. This strategy includes continuously evolving enforcement policies, iterating our Machine Learning risk models, and exercising high-judgement decision-making where we cannot apply automation. Key Job Responsibilities: Use statistical and machine learning techniques and Generative AI to create scalable risk management systems Analyzing and understanding large amounts of Amazon's historical business data for specific instances of risk or broader risk trends Design, development and evaluation of highly innovative models for risk management Working closely with software engineering teams to drive real-time model implementations and new feature creations Working closely with operations staff to optimize risk management operations Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation Tracking general business activity and providing clear, compelling management reporting on a regular basis Research and implement novel machine learning and statistical approaches
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by protecting Amazon customers from hackers and bad actors? Do you want to build advanced algorithmic systems that help manage the trust and safety of millions of customers every day? Are you excited by the prospect of analyzing and modeling terabytes of data and create state-of-the-art algorithms to solve real-world problems? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Amazon Account Integrity team. The Amazon Account Integrity team works to ensure that customers are protected from bad actors trying to access their accounts. Our greatest challenge is protecting customer trust without unjustly harming good customers. To strike the right balance, we invest in mechanisms which allow us to accurately identify and mitigate risk, and to quickly correct and learn from our mistakes. This strategy includes continuously evolving enforcement policies, iterating our Machine Learning risk models, and exercising high-judgement decision-making where we cannot apply automation. Key Job Responsibilities: Use statistical and machine learning techniques and Generative AI to create scalable risk management systems Analyzing and understanding large amounts of Amazon's historical business data for specific instances of risk or broader risk trends Design, development and evaluation of highly innovative models for risk management Working closely with software engineering teams to drive real-time model implementations and new feature creations Working closely with operations staff to optimize risk management operations Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation Tracking general business activity and providing clear, compelling management reporting on a regular basis Research and implement novel machine learning and statistical approaches