Description 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 customer every day? Are you excited by the prospect of analyzing and modeling terabytes of data and create state-of-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. Main responsibilities - Use statistical and machine learning techniques 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 Basic Qualifications - PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience - Experience programming in Java, C++, Python or related language - 3+ years of hands-on predictive modeling and large data analysis experience - Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing - Communication and data presentation skills - Problem solving ability Preferred Qualifications - A PhD in Computer Science, Machine Learning, Statistics or in a highly quantitative field - 3+ years of industry experience in predictive modeling and large data analysis - Strong skills with Python/Spark (or similar scripting language), Java/C++ and SQL - Strong problem solving and dive deep ability - Strong communication, writing and data presentation skills Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us. 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 $222,200/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.