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 Global Sourcing team is looking for Operations Research Scientists to build Amazon’s next generation of inventory planning and 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 and 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. Candidate will be responsible for developing solutions to better manage/optimize the flow of inventory in Amazon's network world wide. This position will focus on identifying and analyzing opportunities to improve existing algorithms and also on the next generation of Amazon’s inventory supply chain optimization systems. This position requires superior analytical thinkers, able to quickly approach large ambiguous problems and apply their technical and statistical knowledge to identify opportunities for further research. 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 a highly competitive salary, growth, 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 team.
• PhD in operations research, management science, statistics, engineering, mathematics, or computer science and 3-5 years of related work experience • Build quantitative mathematical models to represent a wide range of supply chain, transportation and logistics systems. • Ability to develop system prototypes. • Interact with various software and business groups to develop an understanding of their business requirements and operational processes. • 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. • Create computer simulations to support operational decision-making. • 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. Functional thought leader, sought after for key tech decisions.
· Apply the acquired knowledge and business judgment to build decision-supporting and operational tools to improve the bottom line. · Identify areas with potential for improvement and work with internal teams to generate requirements that can realize these improvements. · 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. · Can successfully sell ideas to an executive level decision maker. Can present company-wide technical decisions to the internal technical community and represent the company effectively at technical