Amazon Fulfillment Technologies is looking for a motivated Operations Research Scientist with strong modeling and analytical skills to join our team. As a scientist on our team, you will play an integral role in developing analysis approaches, strategies, and models that improve the efficiency and cost effectiveness of our global network of warehouses (fulfillment centers). Our scientists work with a mix of labor planning, scheduling, forecasting, and operational decision making models. These models present a range of interesting challenges such as: (i) how do we express our problems in a way that can be solved and what are the appropriate solution techniques (ii) how do we quantify the impact of our decisions on operations (iii) how do we improve our model inputs (forecasts, etc) and make our models robust to errors in the same (iv) how do we guarantee that our models can produce solutions fast enough to be practically usable, without having to sacrifice output quality (v) and many more. A more specific list of duties includes:
· Prototyping, analyzing, and maintaining models which are crucial components of our fulfillment strategy.
· Working closely with software development teams as well as business partners to guarantee that models are both implemented correctly and understood by the wider business.
· Driving model improvements to assure these models become the de-facto standard across Amazon’s fulfillment network.
· Identifying and evaluating opportunities to reduce costs and maximize efficiency with new models and techniques.
· Gathering, analyzing, and presenting data to support various business initiatives. Proposing new metrics if the necessary data is not available.
· Helping guide the long term vision of our team, through effective communication with senior management as well with colleagues from computer science, operations research, and business backgrounds.
· PhD or equivalent Master's degree plus 4+ years of research experience in a quantitative field
· Experience investigating the feasibility of applying scientific principals and concepts to business problems and products
· Ph.D. in Operations Research, Statistics, Applied Mathematics, Computer Science, Industrial and Systems Engineering or a related field with peer reviewed publications.
· Good communication skills with both technical and business people. Ability to speak at a level appropriate for the audience. Experience applying these skills in a business setting is a plus.
· A working knowledge of mathematical programming techniques (LPs, QPs, MIPs,..) accompanied by associated expertise in the use of tools and the latest technology for solving them (e.g. XPRESS, CPLEX, Gurobi).
· The ability to implement models and tools through the use of modeling languages (e.g. Python, R, Matlab) or more traditional languages (Java, C++..).
· Experience with statistical analysis.
· Familiarity with logistics concepts – warehouse operations, forecasting, planning, optimization and logistics – gained through work experience or graduate level education.
· Experience working effectively with software engineering teams.
· Experience developing software in traditional programming languages (Python, Java, C++).
· Familiarity with SQL and experience with very large-scale data. The ability to manipulate and analyze data by writing scripts (in Python, R, etc) is a plus.
· Experience with machine learning and data-modeling in a database environment.