Amazon's Cambridge UK based Simulation and Experimentation (SimEx) team is looking for an experienced and passionate Machine Learning Scientist to join our fast-paced stimulating environment, to help invent the future of retail with technology.
The SimEx team is part of the Supply Chain Optimization Technologies (SCOT) organization. The charter of SCOT is to maximize Amazon's return on our inventory investment in terms of Free Cash Flow, and customer satisfaction. We accomplish this by applying simulation, advanced statistical and machine leaning methods, and empirical analysis to predict and evaluate Amazon's inventory needs. The SimEx team builds systems that allow SCOT to answer "what if?" questions about our supply chain, our fulfillment network, and our customers. This puts the SimEx team at the nexus of operations, logistics, capacity planning, and our retail business teams. To learn more about Supply Chain Optimization Technologies (SCOT) at Amazon, watch this amazing video: http://bit.ly/amazon-scot.
As a Machine Learning Scientist on the SimEx team you will work with Researchers, Data Scientists, Data Engineers, Software Engineers, and Product Managers across multiple teams. You will design and develop new machine learning methods that will form the backbone of the simulation and experimentation systems that drive Amazon's retail business forward. Areas of work in this domain include probabilistic modelling, emulation using Gaussian processes, Bayesian optimization and probabilistic numerics, and causal inference. Successful outstanding candidates will bring strong technical and analytical abilities in at least one of these areas, combined with a passion for delivering results for customers, internally and externally. This role requires a high degree of ownership, and a drive to solve some of the most challenging problems in the supply chain.
Amazon is an Equal Opportunity-Affirmative Action Employer Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation.
Undergraduate degree in computer science, software engineering or undergraduate degree in numerical discipline (e.g. physics, maths, engineering).
• PhD (or equivalent experience) in machine learning or statistics
• Ability to communicate scientific results and ideas in writing, as evidenced e.g. by papers in top machine learning conferences (NeurIPS, ICML, AISTATS etc)
• Experience in Python.
• Good communication skills and the ability to work in a team.
• Proven hands on experience in predictive modelling and analysis
Expertise in Bayesian computation, Gaussian processes, kernel methods, surrogate modelling (emulation),
• Experience in python's machine learning and data science stack (tensorflow/pytorch/mxnet, numpy, pandas, matplotlib, scikit-learn, etc)
• Ability to convey rigorous mathematical concepts and considerations to non-experts.
• Ability to distill problem definitions, models, and constraints from informal business requirements; and to deal with ambiguity and competing objectives.
• Strong software development skills.