Applied Scientist, Eu Gts Ras, Gts Ras

• PhD, or a Master's degree and experience in CS, CE, ML or related field
• Experience in building models for business application
• Experience in patents or publications at top-tier peer-reviewed conferences or journals
• Experience programming in Java, C++, Python or related language
• Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

Have you ever wished to build high standard Operations Research and Machine Learning algorithms to optimize one of the most complex logistics network?
Have you ever ordered a product on Amazon websites and wondered how it got delivered to you so fast, and what kinds of algorithms & processes are running behind the scenes to power the whole operation? If so, this role is for you.

The team: Global transportation services, Research and applied science

• Operations is at the heart of the Amazon customer experience. Each action we undertake is on behalf of our customers, as surpassing their expectations is our passion. We improve customer experience through continuously optimizing the complex movements of goods from vendors to customers throughout Europe.
• Global transportation analytical teams are transversal centers of expertise, composed of engineers, analysts, scientists, technical program managers and developers. We are focused on Amazon most complex problems, processes and decisions. We work with fulfillment centers, transportation, software developers, finance and retail teams across the world, to improve our logistic infrastructure and algorithms.
• GTS RAS is one of those Global transportation scientific team. We are obsessed by delivering state of the art OR and ML tools to support the rethinking of our advanced end-to-end supply chain. Our overall mission is simple: we want to implement the best logistics network, so Amazon can be the place where our customers can be delivered the next-day.

The role: Applied scientist, speed and long term network design

The person in this role will have end-to-end ownership on augmenting RAS Operation Research and Machine Learning modeling tools. They will help understand where are the constraints in our transportation network, and how we can remove them to make faster deliveries at a lower cost.

Concretely, you will be responsible for designing and implementing state-of-the-art algorithmic in transportation planning and network design, to expand the scope of our Operations Research and Machine Learning tools, to reflect the constantly evolving constraints in our network. You will enable the creation of a product that drives ever-greater automation, scalability and optimization of every aspect of transportation, planning the best network and modeling the constraints that prevent us from offering more speed to our customer, to maximize the utilization of the associated resources. The impact of your work will be in the Amazon EU global network.

The product you will build will span across multiple organizations that play a role in Amazon’s operations and transportation and the shopping experience we deliver to customer. Those stakeholders include fulfilment operations and transportation teams; scientists and developers, and product managers. You will understand those teams constraints, to include them in your product; you will discuss with technical teams across the organization to understand the existing tools and assess the opportunity to integrate them in your product. You will also be challenged to think several steps ahead so that the solutions you are building today will scale well with future growth and objective (e.g.: sustainability). You will engage with fellow scientists across the globe, to discuss the solutions they have implemented and share your peculiar expertise with them.

This is a critical role and will require an aptitude for independent initiative and the ability to drive innovation in transportation planning and network design. Successful candidates should be able to design and implement high quality algorithm solutions, using state-of-the art Operations Research and Machine Learning techniques. You will have the opportunity to thrive in a highly collaborative, creative, analytical, and fast-paced environment oriented around building the world’s most flexible and effective transportation planning and network design management technology.

Key job responsibilities

• Engage with stakeholders to understand what prevents them to build a better transportation network for Amazon
• Review literature to identify similar problems, or new solving techniques
• Build the mathematical model representing your problem
• Implement light version of the model, to gather early feed-back from your stakeholders and fellow scientists
• Implement the final product, leveraging the highest development standards
• Share your work in internal and external conferences
• Train on the newest techniques available in your field, to ensure the team stays at the highest bar

About the team
GTS Research and Applied Science is a team of 15 scientists and engineers whom mission is to build the best decision support tools for strategic decisions. We model and optimize Amazon end-to-end operations.
The team is composed of enthusiastic members, that love to discuss any scientific problem, foster new ideas and think out of the box. We are eager to support each others and share our unique knowledge to our colleagues.

• Experience using Unix/Linux
• Experience in professional software development

Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice (https://www.amazon.jobs/en/privacy_page) to know more about how we collect, use and transfer the personal data of our candidates.