Amazon Supply Chain forms the backbone of the fastest growing e-commerce business in the world. The sheer growth of the business and the company's mission "to be Earthβs most customer-centric companyβ makes the customer fulfillment business bigger and more complex with each passing year.
The SC Optimization and Automation team within SCOT organization - Supply Chain Optimization Technology - is looking for an exceptionally talented Scientist to tackle complex and ambiguous optimization and forecasting problems for our WW fulfillment network.
The team owns the optimization of our Supply Chain from our suppliers to our customers. We are also responsible for analyzing the performance of our Supply Chain end-to-end and deploying Operations Research, Machine Learning, Statistics and Econometrics models to improve decision making within our organization, including forecasting, planning and executing our network. We work closely with other Supply Chain Optimization Technology teams, with whom we own the systems and the inputs to plan our networks, the worldwide scientific community, and with our internal WW stakeholders within Supply Chain, Transportation, Store and Finance.
We are looking for an experienced candidate having a well-rounded technical/scientific background, and deep expertise in large-scale non-convex non-linear OR optimization (inc. stochastic), as well as forecasting (inc. probabilistic). The candidates should have an history of delivering complex scientific projects end-to-end, and is comfortable in developing long term scientific solutions while ensuring the continuous delivery of incremental model improvements and results in an ever-changing operational environment.
As an Applied Scientist, you will design, develop and deploy robust and scalable scientific solutions via Operations Research and Machine Learning algorithms, especially in the context of stochastic customer demand and other sources of uncertainty requiring to move past deterministic and linear optimization. You will partner with other tech and science teams, operations, finance to identify opportunities to improve our processes in order to drive efficiency improvements in our Fulfillment Center network flows.
This role requires a self-starter aptitude for independent initiative and the ability to influence partner scientific and operational teams so to drive innovation in supply chain planning and execution. You are passionate, results-oriented, and inventive scientist who obsesses over the quality of your solutions and their fast and scalable implementation to address and anticipate customer needs.
Key job responsibilities
- Build state-of-the art, robust, and scalable optimization and forecasting algorithms to drive optimal inventory placement and product flows in non-convex, non-linear, and stochastic optimization settings
- Design and engineer algorithms using Cloud-based state-of-the art software development techniques
- Think multiple steps ahead and develop for long term solutions while continuously delivering incremental improvements to existing ones
- Prototype fast, ensure early adoption via pilots, integrate feedback into the models, and iterate
- Operationalize (i.e. deliver) your science solutions by closely partnering with internal customers, understand their needs/blockers and influence their roadmap
- Lead complex analysis and clearly communicate results and recommendations to leadership
- Act as an active member of the science community by researching, applying and publishing internally/externally the latest OR/ML techniques from both academia and industry
- PhD, or a Master's degree and 5+ experience applying theoretical models in an applied environment
- Experience in solving business problems through machine learning, data mining and statistical algorithms
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- Experience programming in Java, C++, Python or related language
- 5+ years experience in commercial OR tools (e.g. CPLEX, Gurobi, XPRESS)
- 5+ years experience in developing OR algorithm for non-convex and non-linear optimization problems
- 2+ years experience with Stochastic Optimization algorithms (e.g. Stochastic Linear Programming, Stochastic Dynamic Programming) and ML for Probabilistic Forecasting
- Sharp analytical abilities, excellent written and verbal communication skills
- Ability to handle ambiguity and fast-paced environment- Experience in professional software development
- Reinforcement Learning
- Experience with machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance
- Experience diving into data to discover hidden patterns and of conducting error/deviation analysis
- Familiarity with Operations concepts - Planning, Forecasting, Optimization, and Customer experience - gained through work experience or graduate level education
- Experience with AWS services including S3, Redshift, Sagemaker, EMR, Kinesis, Lambda, and EC2
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