Duration: 4-6 months
Amazon is a company of builders. A philosophy of ownership carries through everything we do — from the proprietary technologies we create to the new businesses we launch and grow. You’ll find it in every team across our company; from providing Earth’s biggest selection of products to developing ground-breaking software and devices that change entire industries, Amazon embraces invention and progressive thinking. Amazon is continually evolving; it’s a place where motivated employees thrive, and ownership and accountability lead to meaningful results. It’s as simple as this: we pioneer.
With every order made and parcel delivered, customer demand at Amazon is growing. And to meet this demand, and keep our world-class service running smoothly, we’re growing our teams across Europe. Delivering hundreds of thousands of products to hundreds of countries worldwide, our Operations teams possess a wide range of skills and experience and this include software developers, data engineers, research scientists, and more.
About these internships:
Whatever your background, if you are excited about modeling huge amounts of data and creating state of the art algorithms to solve real world problems, if you have a passion for working at the edge of computational methods and statistical modelling, if you enjoy solving challenges deploying huge-scale computer simulations, and if you’re motivated by results and driven enough to achieve them, Amazon is a great place to be. Because it’s only by coming up with new ideas and challenging the status quo that we can continue to be the most customer-centric company on Earth, we’re all about flexibility: we expect you to adapt to changes quickly and we encourage you to try new things.
Amazon is looking for ambitious and enthusiastic PhD students to join our unique world as Applied Scientist interns. An Amazon Cambridge internship will provide you with an unforgettable experience in a fast-paced, dynamic and international environment; it will boost your resume and will provide a superb introduction to our activities.
As an Applied Science intern in Simulation and Experimentation, you’ll work with our machine learning team on a project that pushes the state-of-the-art in machine learning for simulation. You’ll have the chance to work as part of a small team delivering a project that innovates for our customers, by helping our supply chain teams understand, tune, optimize and deliver the Amazon network.
You’ll put your machine learning and statistics skills to the test, contributing to a project that improves the functionality and level of service that teams provides to our customers. This could include:
· Building predictive Bayesian models that can be used to help make smart decisions about what to simulate
· Developing new computational methods that improve the speed with which we can deliver decisions, and expand the level of complexity that our models can handle
· Developing new agent systems that work to optimize, tune and calibrate simulation systems against the real world
· Diving into the computational aspects of Bayesian modelling to enable algorithms that scale across our cloud compute services
We want to hire the world’s brightest minds, and offer them an environment in which they can learn and help improve the experience for our customers.
· You are currently working towards a PhD in Machine Learning, Statistics, or a closely related field.
· Excellent written and verbal communication skills in English.
· You must have the right to work in the country you are applying for.
· Experience in using numpy/scipy/matplotlib and the python science ecosystem.
· You are able to use at least one machine learning frame work from Tensorflow, Pytorch, MXnet, Jax.
· Broad knowledge of statistics its application. You’re confident discussing aspects of generalized linear models, clustering, ANOVA, confidence intervals.
· You understand the role of the posterior distribution in Bayesian modelling, and are knowledgeable about at least one method for computing it.
· Knowledge of supervised learning methods (linear and logistic regression, decision trees, random forests, support vector machines, graphical models, neural networks, etc.) as well as unsupervised learning methods (K-means, hierarchical clustering, principal components, etc.)
· You work well in a team, with the ability to pick up and adapt modeling methods from other disciplines or leverage methods from other skilled colleagues.
· Your scientific work is reproducible and documented. You use version control. You use literate programming where appropriate.
· You are a good scientific communicator: you’re able to communicate key results and their impact succinctly.
· Knowledge of kernel methods and Gaussian processes. Experience with state-of-the-art Bayesian computation, for example Hamiltonian Monte Carlo, variational inference.
· An understanding of the role of surrogate models in uncertainty quantification, Bayesian optimization and model calibration.
· Experience in SQL and using databases in a business environment with large-scale, complex datasets.
· Familiarity with supply chain management concepts – forecasting, planning, optimization, and logistics.
Our employees’ safety is our top priority and we continue to monitor the global health situation. Because of this, your internship might be based in one of our facilities or it could be delivered virtually. Should it need to be delivered virtually, we have developed a program that ensures you will continue to have a meaningful and positive internship experience.
Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decision based on your experience and skills. We value your passion to discover, invent, simplify and build. We welcome applications from all members of society irrespective of age, sex, disability, sexual orientation, race, religion or belief.
By submitting your resume and application information, you authorize Amazon to transmit and store your information in the Amazon group of companies’ world-wide recruitment database, and to circulate that information as necessary for the purpose of evaluating your qualifications for this or other job vacancies.