[DMANET] [HIRING] Research/Applied Scientist for Surface Transportation / Sustainability @ Amazon EU in Luxembourg

Hello everyone,

Amazon EU has an opening for a research / applied scientist at the Amazon EU in Luxembourg to work on problems related to surface transportation and sustainability (i.e. the middle-mile logistics part of Amazon's climate pledge).

You will be joining a quickly growing multi-national research team that tackles challenging problems related to logistics and sustainability on a world wide scale. Our team is in the fast growing Amazon EU headquarters in Luxembourg City, which has more than 3000 employees already.

Luxembourg is a small country in the heart of Europe with a very international population and excellent public health care and social security, free public transport and very low crime. It is well connected to all the major European cities. Luxembourg has three official languages (Luxembourgish, French, German) and English is widely spoken as well.

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.

We are very willing to sponsor visas!

BASIC QUALIFICATIONS

* PhD in Operations Research, Machine Learning, Statistics, Applied Mathematics, Engineering, Computer Science or other field related to algorithms.
* Excellent written and verbal communication skills. Ability to communicate at a level appropriate to the audience.
* Experience implementing algorithms in traditional programming languages (C++/ Java/ python)
* Comfortable to tradeoff complexity and efficiency of solution methodologies, according to the requirements of the problem. Ability to deal with ambiguity.
* Experience designing and implementing models and algorithms for one or more:
* Combinatorial optimization problems (e.g., scheduling, vehicle routing, and facility location).
* Continuous optimization problems (e.g., linear programming, convex programming, non-convex programming).
* Predictive analytics (e.g., forecasting, time-series, neural networks)
* Prescriptive analytics (e.g., stochastic optimization, bandits, reinforcement learning).

PREFERRED QUALIFICATIONS
* Detailed knowledge of optimization methods including linear and mixed-integer programming, network modeling, constraint programming, approximation algorithms, and advanced heuristic techniques.
* Expertise on MIP strategies to customize and leverage commercial algorithms and adapt them as required.
* Detailed knowledge of forecasting techniques with time-series tools, including ARIMA models, exponential smoothing, LSTM, CNNs.
* Expertise on policy optimization techniques, including reinforcement learning, deep Q-learning, bandits, and online optimization.
* Experience implementing models and analysis tools through the use of high-level modeling languages (e.g. R, Matlab as examples).
* Experience collecting, processing and combining big data with appropriate methodologies (e.g. Hadoop, Map-Reduce)

To apply please use the link:
https://amazon.jobs/en/jobs/1667294/research-scientist

Best regards,
Martin Gross
Amazon Transportation Services
Senior Applied Scientist
Sustainability & Surface Transportation Research



Amazon EU societe a responsabilite limitee, 38 avenue John F. Kennedy, L-1855 Luxembourg, R.C.S. Luxembourg n
B101818, autorisation d'etablissement en qualite de commercante n 134248, TVA LU 20260743




**********************************************************
*
* Contributions to be spread via DMANET are submitted to
*
* DMANET@zpr.uni-koeln.de
*
* Replies to a message carried on DMANET should NOT be
* addressed to DMANET but to the original sender. The
* original sender, however, is invited to prepare an
* update of the replies received and to communicate it
* via DMANET.
*
* DISCRETE MATHEMATICS AND ALGORITHMS NETWORK (DMANET)
* http://www.zaik.uni-koeln.de/AFS/publications/dmanet/
*
**********************************************************