Science at Amazon enables new customer experiences, addresses existing customer pain points, complements engineering and product disciplines, and is a critical functional skill for all Amazon businesses. It is this focus on the customer, and the company’s ability to have impact at global scale that attracts some of the brightest minds in artificial intelligence, machine learning, and related fields.
Amazon scientists are conducting cutting edge research in areas ranging from machine learning to operations, conversational AI, robotics, quantum computing, and more.
Take deep dives into the latest research from Amazon scientists. including in depth looks at research that has been accepted at leading scientific conferences around the world.
The latest news about scientific innovation at Amazon, including in-depth behind the science features, awards, and recognitions.
Amazon is a great place to practice science and have real business impact, but that’s only one part of the story. Our scientists continue to publish, teach, and engage with the worldwide research community.
Amazon researchers regularly contribute to the broader scientific community through the public release of code and datasets.
Our scientists are active in conferences worldwide, where they look forward to contributing to — and learning from — the latest research, as well as engaging with the global science community.
Whether you’re a faculty member, student, developer, thought leader or a policy maker, Amazon offers a number of ways to engage with the company’s science community.
The company recruits talent from around the world for applied scientists, data scientists, economists, research scientists, scholars, academics, PhDs, and interns.
Christopher Stratchey wrote, "The separation of practical and theoretical work is artificial and injurious. Much of the practical work done in computing, both in software and in hardware design, is unsound and clumsy because the people who do it have not any clear understanding of the fundamental design principles of their work. Most of the abstract mathematical and theoretical work is sterile because it has no point of contact with real computing." Our customer-obsessed science strategy reliably nudges me back towards the intersection of the practical and theoretical. That's where the really game-changing work is at.
It means first developing a conviction that the problem we are working on is or will be truly important to customers. It's like asking the five whys — all starting with, ‘Who cares about the problem, and why should they care?’ — before getting down to a mathematical model. Once we are convinced about the value, it's about developing the right science to address the problem — and the problems that have the most customer impact in the long-term often require exciting new science and systems. This helps us focus on science that will really move the needle for our customers and stand the test of time.
Customer-obsessed science means to always put yourself in the customer's shoes to improve the experience. It also means listening to customer pain points, and inventing on their behalf. They will tell you what they don't like, but it is up to us to provide solutions to delight them. Just Walk Out is a prime example of innovative solution addressing the "nobody likes to wait in line" customer pain point.
Customer-obsessed science aims to solve customer problems and improve customer experiences. It is aligned with business priorities and brings value to our business. It is science that works backwards from customer needs and pain points, as opposed to forward from technology. It's important not to confuse customer-obsessed science with science that has a short time-horizon — customer-obsessed science be focused on the long-term.
Customer-obsessed science means that you focus on understanding the customer's problem and bringing the best scientific tools to solve the problem. It means that you are not dogmatic about methods, but seek to apply the best method or combination of methods to solve the customer's problem. You invent and simplify, seeking expertise by partnering with others if the best method(s) is not your specialty.
Research and development that is grounded on real-world challenges and customer-facing problems. Only by working backwards from the customer, including defining metrics that characterize customer experience, we can ensure that our scientific innovations have measurable impact on customers’ lives.
Working backwards from customers means advancing state of art that solves specific customer pain points or builds new delights. The key difference is that we do not start with a solution and then look for problems where that solution can be applied. We start with the desired experience, identify key problems to solve, and then invent novel approaches to solve those problems.
Customer obsession in science means applying the scientific method in service of our customers. Working backwards from the customer; their needs, wants, and pain points, is focusing our work on scientific innovation that is truly impactful. But science is a creative endeavor -- we often find surprises and new insights along the way. As we do, we continuously evaluate new ways to delight our customers.
Customer-obsessed science is about anticipating customers' needs by devising innovative solutions to challenging problems that customers do not yet realize they have or will have. This allows us to respond quickly with enhanced services when these needs do arise.
I was always attracted to practical science — calculating where a projectile will land, how current flows in a circuit, what determines supply and demand. I love understanding how the world works and using this knowledge to make things better. And this is exactly what Amazon's customer-obsessed science is all about, working backwards from what customers value and using science to innovate and make their lives better. It's what great science is all about.
Inventing devices and services that improve the health and wellness of everyone on the planet.
Customer-obsessed science means inventing and applying scientific approaches to understand and solve customer problems. It’s not just about coming up with the best algorithm or model you can think of, but proving it with sound methodology and ensuring it’s applicable to real-world challenges. This is core to everything that we do at Amazon, and it’s what makes us different. We start with our customers and work backwards from their needs, testing and improving our products and services based on their behavior and feedback.