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Seattle版 - Amazon 亚马逊 机器学习 openings (转载)
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M**********n
发帖数: 314
1
【 以下文字转载自 JobHunting 讨论区 】
发信人: MultiMillion (Multi Million), 信区: JobHunting
标 题: Amazon 亚马逊 机器学习 openings
发信站: BBS 未名空间站 (Thu May 17 18:07:30 2018, 美东)
Multiple Machine Learning Scientist positions are opened at Amazon
Transaction Risk Management System (TRMS). Interested? Send your resume to
[email protected] If your background matches our positions, we will reach
out to you. Thanks.
我们部门有多个机器学习的opening, 有兴趣的同学可以将简历发给我 [email protected]
gmail.com 谢谢。
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
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