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Senior Manager, Science, EC2 Placement Science

Job ID: 1878982 | Amazon Dev Centre (London) Ltd


Job summary
Lead a group of scientists and engineers to develop new ways to optimize how EC2 bin packs instances on to the underlying hardware. Apply techniques from combinatorial optimization, operations research and machine learning to help us push the state of the art and deliver meaningful results for customers.

Every time an EC2 instance is launched or capacity is reserved, our systems optimize across a range of objectives that cross-cut the AWS organization. As the Senior Manager for EC2 Placement Science you bring structure to ambiguous business problems and apply your scientific and organizational acumen to deliver results. You will define and execute a long-term science vision for our organization, driven from our customers' needs. Because of the broad span of concerns that come together in EC2 instance placement, this role rewards the curious.

Key job responsibilities

  • Partner with senior leaders across the organization to frame business problems, establish a vision, and execute on your roadmap.
  • Lead a group of applied scientists and software engineers to deliver optimization solutions to production.
  • Hire and develop top talent, provide technical and career development guidance to both scientists and engineers in the organization.
  • Set the scientific culture and lead best practice within organization.
  • Provide thought leadership in the organization and advocate for new initiatives


  • PhD in a quantitative discipline such as Statistics, Operations Research, Computer Science, or equivalent experience
  • At least 5 years experience leading scientists and engineers
  • At least 10 years experience developing optimization or machine learning solutions to customer problems
  • Demonstrated experience with at least one modern programming language, such as Python, Scala, or Java


One or more of the following help you stand out:

  • Demonstrated technical leadership with 10+ years of exceptional, hands-on experience in building production optimization or machine learning systems at scale
  • Published research work in academic conferences or industry circles
  • Excellent oral and written communication skills, with the ability to communicate complex technical concepts and solutions to all levels of the organization
  • Experience building large-scale production distributed systems
  • Experience with big data technologies, such as Spark, Hadoop, Apache Samza and AWS.

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