Are you up to the task of delivering innovative solutions that manages a supply chain of millions of unique products involving hundreds of thousands of suppliers and tens of millions of customers around the world? Amazon’s Supply Chain Optimization team is looking for Operations Research Scientists to build Amazon’s next generation of supply chain solutions. We are part of Supply Chain Optimization Technologies (SCOT): https://www.youtube.com/watch?v=ncwsr1Of6Cw&feature=youtu.be
Our systems use real time, large scale optimization techniques to automatically procure and source the product from suppliers, optimize the flow of goods across the supply chain to minimize the cost and lead time for customers. Our team is focused on saving hundreds of millions of dollars using cutting edge science, machine learning, and scalable distributed software on the Cloud that automates and optimizes supply chain under the uncertainty of demand, pricing and supply.
Our systems are built entirely in-house, and are on the cutting edge in automated large scale supply chain planning, optimization and simulations. We are unique in that we’re simultaneously developing the science of supply chain planning and solving some of the toughest computational challenges at Amazon. Unlike many companies who buy existing off-the-shelf planning systems, we are responsible for studying, designing, and building systems to suit Amazon’s needs. Our team members have an opportunity to be on the forefront of supply chain thought leadership by working on some of the most difficult problems in the industry with some of the best product managers, research scientists/statisticians/economists and software developers in the business.
We are seeking an experienced operations research scientist. This position requires analytical thinkers who are able to approach ambiguous problems, seek guidance from other senior researchers, and apply their technical and statistical knowledge in providing a working solution. You should be able to independently mine and analyze data, and be able to use any necessary programming and statistical analysis software to do so. Successful candidates must thrive in fast-paced environments which encourage collaborative and creative problem solving, be able to measure and estimate risks, constructively critique peer research, and align research focuses with the Amazon’s strategic needs.
In joining our team, you'll enjoy working closely with smart engineers and researchers along with other benefits. We have a creative and comfortable work environment and this is your opportunity to be part of a fast-paced and growing technology and research team.
• PhD in operations research, management science, statistics, engineering, mathematics, or computer science • Build quantitative mathematical models to represent a wide range of supply chain, transportation and logistics systems. • Ability to develop system prototypes. • Implement the models and tools through the use of modeling languages and by engineering code in software languages. • Perform quantitative, economic, and numerical analysis of the performance of these systems under uncertainty using statistical and optimization tools such as R and XPRESS to find both exact and heuristic solution strategies for optimization problems. • Apply theories of mathematical optimization, including linear programming, combinatorial optimization, integer programming, dynamic programming, network flows and algorithms to design optimal or near optimal solution methodologies to be used by in-house decision support tools and software. • Optimization: Candidate should know math modeling, fundamental optimization (e.g., line search, LP, DP, network flows) and basic NLP-IP/combinatorial optimization very well. He/She should know solution methods for LP (e.g., simplex) and IP (e.g., branch and bound, Lagrangean relaxation). Additional knowledge, such as decomposition techniques, interior point methods and heuristic optimization, is a plus. • Probability and statistics: Candidate should know basic probability, expectation and conditional expectation, common statistics, exploratory data analysis, linear regression, hypothesis testing. Additional knowledge of probability is a plus. • Programming: Exposure to programming languages and tools such as C, Matlab, VBA. Candidate should be able to come up with working examples/prototypes independently. It is a plus to know Unix, SQL, SAS and R. • Communication: Able to convey mathematical results in plain English, being able to clarify and formalize complex problems.
Research: Preferred research areas includes inventory optimization, supply chain management, and network optimization. Other relevant areas of research are revenue management, pricing optimization, forecasting, applications of stochastic and approximate dynamic programming, applications of game theory to Supply chain management, decision analysis, system dynamics, and econometrics.