Data Scientist , Automated Inventory Management

Amazon Science
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Job Description

Amazon’s Automated Inventory Management (AIM) team is looking for passionate, hard-working, and talented individuals to join our fast paced, stimulating environment to help invent the future of business ownership with Technology, and to translate big data into actionable insights.

The AIM team is part of the Supply Chain Optimization Technology (SCOT) Team within the Operations Organization. The charter of the SCOT team is to maximize Amazon’s return on our inventory investment in terms of Free Cash Flow and customer satisfaction. We accomplish this by determining how much inventory Amazon needs of each SKU and what to do if we have too much. This puts the Inventory Planning group at the nexus of operations, logistics, capacity planning, and our retail business teams.

As a Senior Data Scientist on the AIM team, you will design quantitative systems, forecasting models and solve real world problems using the latest machine learning techniques. You will also work with a team of Product Managers, Business Intelligence Engineers and Software Engineers to research and build accurate predictive models and deploy automated software solutions to provide insights to business leaders at the most senior levels throughout the company. You will build models that make our data more actionable and help us make complex business decisions at scale.

Key responsibilities:

  • Implement statistical and machine learning methods to solve complex business problems
  • Research new ways to improve predictive and explanatory models
  • Directly contribute to the design and development of automated prediction systems and ML infrastructure
  • Build models that can detect supply chain defects and explain variance to the optimal state
  • Collaborate with other researchers, software developers, and business leaders to define the scientific roadmap for this team

BASIC QUALIFICATIONS

  • Master’s degree (or Bachelors degree or 5+ years of experience) in a quantitative discipline such as Statistics, Mathematics, Data Science, Business Analytics, Economics, Finance, Engineering, or Computer Science
  • 2+ years of experience working as a data scientist or a similar role involving data extraction, analysis, statistical modeling, and communication
  • 2+ years of experience using data querying languages (e.g. SQL, Hadoop/Hive)
  • Master or Ph.D. degree in Statistics, Applied Mathematics, Operation Research, Economics or a related quantitative field
  • 5+ years of hands-on experience in predictive modeling and machine learning
  • Proficiency in model development, model validation and model implementation
  • Proficiency working with R or Python
  • Experience leading, mentoring, and growing teams of scientists

PREFERRED QUALIFICATIONS

  • Extensive knowledge and practical experience in several of the following areas: machine learning, time-series, statistics, deep learning, recommendation systems.
  • Experience processing, filtering, and presenting large quantities (Millions to Billions of rows) of data.
  • Practical experience with big-data processing libraries, eg. Apache Spark, Apache Beam, Hive, Apache Pig, Hadoop or similar

The base pay range for this position in Colorado is $119,300 - 160,000/yr and in New York City is $119,300 - 160,000/yr. Pay is based on market location and may vary depending on job-related knowledge, skills, and experience. A sign-on bonus and restricted stock units may be provided as part of the compensation package, in addition to a full range of medical, financial, and other benefits, dependent on the position offered. Applicants should apply via Amazon's internal or external careers site.

Company Info.

Amazon Science

Amazon Science examines the company’s approach to scientific innovation. Amazon believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields.

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