Data Engineer (Luxembourg), ATS EU Research Science
Amazon
ATHENS, GRC
πριν από 5 μέρες

Job summary

Have you ever wondered how Amazon delivers timely and reliably hundreds of millions of packages to customer’s doorsteps? Are you passionate about data and mathematics, and hope to impact the experience of millions of customers?

Are you obsessed with designing simple algorithmic solutions to very challenging problems?

If so, we look forward to hearing from you!

Amazon Transportation Services is seeking a talented Data Engineer. As a key member of the central Research Science Team of ATS operations, these persons will be responsible for implementing and maintaining algorithmic solutions based on data and mathematics for optimizing the middle-mile Amazon transportation network.

The job is opened in the EU Headquarters in Luxembourg, designed to maximize interaction with the team and stakeholders, but we will consider applicants with remote work requirements as well.

As we believe diversity strength, we encourage applicants of diverse backgrounds.

Key job responsibilities

Key job responsibilities

  • Work with Research scientists to develop optimization models and deploy them in production at the highest standards of Amazon.
  • Maintain and troubleshoot existing research science products in close collaboration with business customers.
  • Lead complex time-bound, long-term as well as ad-hoc analyses to assist decision making.
  • Communicate to leadership results from business analysis, strategies and tactics.
  • A day in the life

  • You will be deep-diving complex mathematical algorithms to answer key business questions.
  • You will be developing and testing prototype solutions with above algorithmic techniques.
  • You will be scavenging information from the sea of Amazon data to improve these solutions.
  • You will be meeting with other scientists, engineers, stakeholders and customers to enhance the solutions and get them adopted.
  • About the team

    The Research Science (RS) team of ATS EU is looking for candidates who are looking to impact the world with their programming, mathematical and data-driven skills.

    ATS stands for Amazon Transportation Service, we are the middle-mile planners : we carry the packages from the warehouses to the cities in a limited amount of time to enable the Amazon experience .

    As the core research team, we grow with ATS business to support decision making in an increasingly complex ecosystem of a data-driven supply chain and e-commerce giant.

    We take pride in our algorithmic solutions :

  • We schedule more than 1 million trucks with Amazon shipments annually; our algorithms are key to reducing CO2 emissions, protecting sites from being overwhelmed during peak days, and ensuring a smile on Amazon’s customer lips.
  • We do not shy away from responsibility. Our mathematical algorithms provide confidence in leadership to invest in programs of several hundreds millions euros every year.
  • Above all, we are having fun solving real-world problems, in real-world speed, while failing & learning along the way.
  • We employ the most sophisticated tools :

  • We use modular algorithmic designs in the domain of combinatorial optimization, solving complicated generalizations of core OR problems with the right level of decomposition, employing parallelization and approximation algorithms.
  • We use deep learning, bandits, and reinforcement learning to put data into the loop of decision making.
  • We like to learn new techniques to surprise business stakeholders by making possible what they cannot anticipate. For this reason, we work closely with Amazon scholars and experts from Academic institutions.
  • We are obsessed with the highest standards of customer-oriented research :

  • We target publications at the most prestigious conferences such as (INFORMS and NeurIPS)
  • We code our prototypes to be production-ready
  • We prefer provably optimal solutions than heuristics, though we settle for heuristics when performance dictates it. Overall, we appreciate the value of correct modeling.
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