Amazon Applied Scientist Supply Chain Optimization 

United Kingdom, England, London 

744339821

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.
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- Lead complex analysis and clearly communicate results and recommendations to leadership

- PhD, or a Master's degree and 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
- 3+ years experience in commercial OR tools (e.g. CPLEX, Gurobi, XPRESS)
- 3+ 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