Applied Scientist - Amazon - Seattle - Wizbii

Applied Scientist

  • Durch Amazon
  • Seattle (United States)
  • Informationssysteme

Job 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 preventing eCommerce fraud? Do you want to build advanced algorithmic systems that help manage the trust and safety of millions of transactions 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 own end-to-end business problems/metrics and directly impact the profitability of the company? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Amazon Transaction Risk Management Systems group.

Major 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

Seeked profile


· A MS in CS machine learning, Statistics, Operational research or in a highly quantitative field
· 3+ years of hands-on experience in predictive modeling and large data analysis
· Communication and data presentation skills
· Problem solving ability

Amazon is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation

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