Applied Scientist - Denied Party Screening
- Arlington, VA
Our mission is to prevent denied parties from transacting with Amazon businesses. We build automatic mechanisms to detect and prevent prohibited transactions with the denied parties using a diverse set of algorithms and machine learning techniques. We screen over a half a billion events every day, integrate with Tier 1 systems and deal with unique scaling challenges. We are still day 1 and have an exciting road map to build ML powered detection and resolution framework to help scale Amazon for years to come.
As a Applied Scientist, You will research, design and develop innovative Machine Learning models to prevent denied parties from transacting on Amazon. You will be working in a highly collaborative environment partnering with various science, product management, engineering, operations, finance, business intelligence and analytics teams to develop ML systems and other analytical tools. You will need to understand the business requirements and translate them into complex analytical outputs. You will design tests to explain performance of models from revenue and cost perspective. You will create production grade ML models to capture features impacting performance. You should be comfortable building prototypes, testing and improving them given the feedback from the real time data. You should be able to present your model and findings to a various range of stakeholders.
Amazon.com is an Equal Opportunity Employer - Minority / Women / Disability / Veteran / Gender Identity / Sexual Orientation / Age.
• PhD or equivalent Master's Degree plus 4+ years of experience in CS, CE, ML or related field
• 2+ years of experience of building machine learning models for business application
• Experience programming in Java, C++, Python or related language
• Bachelor's degree in Statistics, Applied Math, Operations Research, Engineering, Computer Science, or a related quantitative field
• 3+ years of working experience as a Data Scientist
• Hands-on coding experience in scripting language such as and data manipulation/analysis libraries for analyzing and modeling data.
• Experienced in handling large data sets using SQL and databases in a business environment.
• Experience implementing ML models in production environment with high latency and volume requirements
• Strong programming fundamentals in data handling, problem solving, algorithm design and complexity analysis
• Experience with defining research and development practices in an applied environment
• Peer reviewed scientific publications
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