Applied Scientist - Operations Research/Optimization, Sales Planning and Inventory Optimization Science
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Amazon
Seattle, WA (In Person)
Full-Time
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Job Description
DescriptionAre you interested in working with top talents in Optimization, Operations Research and Supply Chain to help Amazon to efficiently match our Devices with worldwide customers? We have challenging problems and need your innovative solutions to make tremendous financial impacts! The Amazon Demand Science Optimization organization is looking for an Applied Scientist with background in Operations Research, Optimization, Supply Chain, Simulation, and Gen AI to support science efforts to integrate across inventory management functionalities. Our team is responsible for science models (both deterministic and stochastic) that power world-wide inventory allocation, promotion optimization for Amazon Devices business that includes Echo, Kindle, Fire Tablets, Amazon TVs, Amazon Fire TV sticks, Ring, and other smart home devices. We formulate and solve challenging large-scale financially-based optimization problems which ingest demand forecasts and produce optimal price promotion strategies, procurement, production, distribution, and inventory management plans. In addition, we also work closely with the demand forecasting, material procurement, production planning, finance, and logistics teams to co-optimize the inventory management and supply chain for Amazon Devices given operational constraints. Key job responsibilities The successful candidate will be a self-starter, comfortable with ambiguity, with strong attention to detail, and an ability to work in a fast-paced and ever-changing environment and a desire to help shape the overall business.
Responsibilities include:
- Design and develop advanced mathematical, simulation, and optimization models and apply them to define strategic and tactical needs and drive appropriate business and technical solutions in the areas of inventory management and distribution, network flow, supply chain optimization, and demand planning
- Apply mathematical optimization techniques (linear, quadratic, SOCP, robust, stochastic, dynamic, mixed-integer programming, network flows, nonlinear, nonconvex programming) and algorithms to design optimal or near optimal solution methodologies to be used by in-house decision support tools and software
- Research, prototype and experiment with these models by using modeling languages such as Python; participate in the production level deployment
- Create, enhance, and maintain technical documentation, and present to other Scientists, Product, and Engineering teams
- Lead project plans from a scientific perspective by managing product features, technical risks, milestones and launch plans
- Influence the organization's long-term roadmap and resourcing, onboard new technologies onto Science team's toolbox, mentor other Scientists About the team Amazon Science https://www.linkedin.com/showcase/amazonscience/posts/?feedView=allBasic Qualifications
- PhD, or Master's degree and 4+ years of science, technology, engineering or related field experience
- 3+ years of building models for business application experience
- 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 computingPreferred Qualifications
- Experience in optimization mathematics such as linear programming and nonlinear optimization
- PhD in math/statistics/engineering or other equivalent quantitative discipline, or a Associate's degree or above and experience applying optimization models in for business decision support or in optimized supervisory control
- Experience in technical support, or experience with training and deploying machine learning systems to solve large-scale optimizations
- Experience developing, deploying and managing AI products at scale
- Experience in building optimization models and implementing them on OR tools (e.
- 142,800.00
- 193,200.
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