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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
- Master's degree
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning
- Experience in building machine learning models for business application
- Experience in applied research
- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.
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