Michael Kearns and Aaron Roth seated at a table in front of a large chalk board.
Michael Kearns, left, and Aaron Roth, right, are the co-authors ofThe Ethical Algorithm: The Science of Socially Aware Algorithm Design. Kearns and Roth are leading researchers in machine learning, University of Pennsylvania computer science professors, and Amazon Scholars.
University of Pennsylvania

Amazon Scholars Michael Kearns and Aaron Roth discuss the ethics of machine learning

Two of the world’s leading experts on algorithmic bias look back at the events of the past year and reflect on what we’ve learned, what we’re still grappling with, and how far we have to go.

In November of 2019, University of Pennsylvania computer science professors Michael Kearns and Aaron Roth released The Ethical Algorithm: The Science of Socially Aware Algorithm Design. Kearns is the founding director of the Warren Center for Network and Data Sciences, and the faculty founder and former director of Penn Engineering’s Networked and Social Systems Engineering program. Roth is the co-director of Penn’s program in Networked and Social Systems Engineering and co-authored The Algorithmic Foundations of Differential Privacy with Cynthia Dwork. Kearns and Roth are leading researchers in machine learning, focusing on both the design and real-world application of algorithms.

Their book’s central thesis, which involves “the science of designing algorithms that embed social norms such as fairness and privacy into their code,” was already pertinent when the book was released. Fast forward one year, and the book’s themes have taken on even greater significance.

Amazon Science sat down with Kearns and Roth, both of whom recently became Amazon Scholars, to find out whether the events of the past year have influenced their outlook. We talked about what it means to define and pursue fairness, how differential privacy is being applied in the real world and what it can achieve, the challenges faced by regulators, what advice the two University of Pennsylvania professors would give to students studying artificial intelligence and machine learning, and much more.

Q. How has the narrative around designing socially aware algorithms evolved in the past year, and have the events of the past year altered your outlooks in any way?

Aaron Roth: The main thesis of our book, which is that in any particular problem you have to start by thinking carefully about what you want in terms of fairness or privacy or some other social desideratum, and then how you relatively value things like that compared to other things you might care about, such as accuracy—that fundamental thesis hasn't really changed.

Now with the coronavirus pandemic, what we have seen are application areas where how you want to manage the trade-off between accuracy and privacy, for example, is more extreme than we usually see. So, for example, in the midst of an outbreak, contact tracing might be really important, even though you can't really do contact tracing while protecting individual privacy. Because of the urgency of the situation, you might decide to trade off privacy for accuracy. But because the message of our book really is about thinking things through on a case-by-case basis, the thesis itself hasn't changed.

Michael Kearns: The events of the last year, in particular coronavirus, the resulting restrictions on society and the tensions around these restrictions, and all of the recent social upheaval in the United States, clearly has made the topics of our book much more relevant. The book has focused a lot of attention on the use of algorithms for both good and bad purposes, including things like contact tracing or releasing statistics about people's movements or health data, as well as the use of machine learning, AI, and algorithms more generally for applications like surveillance.

Since our book, at a high level, is about the tensions that arise when there's a battle between social norms like equality or privacy and the use of algorithms for optimizing things like performance or error, I don't think anything in the last year has changed our thinking about the technical aspects of these problems. It's clear that society has been forced to face these problems in a very direct way because of the events of the last year, in a way that we really haven't before. In that sense, our timing was very fortunate because the things we're talking about are more relevant now than ever.

Q. How does that affect your ability to define fairness? Is that something that can ever be a fixed definition, or does it need to be adjusted as events or specific use cases dictate?

Kearns: There's not one correct definition of fairness. In every application you have to think about who the parties are that you're trying to protect, and what the harms are that you're trying to protect them from. That changes both over time and in different scenarios.

Roth: Even before the events of the last year, fairness was always a very context- and beholder- dependent notion. One society might be primarily concerned about fairness by race, and another might be primarily concerned about fairness by gender, and a different community might have other norms. The events of the last year have highlighted cases in which not only will things vary over space or communities, but also over time.

People's attitudes about relatively invasive technologies like contact tracing might be quite different now than they were a year ago. If a year ago I told you, “Suppose there was some disease that some people were catching and the most effective way of tamping it down was to do contact tracing.” Many people might have said, “That sounds really invasive to me”, but now that we've all been through one of the alternatives—being on lock down for six months—people's minds might be changed. We’ve definitely seen norms around privacy for health-related data change.

Q. Standard setting bodies have a significant challenge when it comes to auditing algorithms. Given the scope of that challenge, what needs to happen to allow those groups to do that effectively?

Roth: Although it hasn't happened yet, regulatory agencies are thinking about this, and are reaching out to people like us to help them think about doing this in the right way. I don't know of any regulatory agency that is ready yet to audit algorithms at-scale in sensible ways of the technical sort we discuss in the book. But there are regulatory agencies that have gotten the idea that they should be gearing up to do this, and those agencies have started preliminary movements in that direction.

Kearns: Many of the conversations we've had with standard setting bodies make it clear they're realizing that, collectively, they've technologically fallen behind the industries that they regulate. They don't have the right resources or personnel to do some of the more technological types of auditing. But in these conversations, it's also become clear to us that, even if you could snap your fingers and get the right people and the right resources, it will only be part of a broader framework.

Other important pieces involve becoming more precise about best practices, and also thinking carefully about what those specifications should look like. Let me give a concrete example: One of the things that we argue in our book is that there are many laws and regulations in areas like consumer finance, for instance, that try to get at fairness by restricting what kinds of inputs an algorithm can use. These laws and regulations say, “In order to make sure that your model isn't racially discriminatory, you must not use race as a variable.” But, in fact, not using race as a variable is no guarantee that you won't build a model that's discriminatory by race. In fact, it can actually exacerbate that problem. What we advocate in the book is, rather than restricting the inputs, you should specify the behavior you want as outputs. So instead of saying, “Don't use race”, say instead, “The outputs of the models shouldn't be discriminatory by race.”

Q. Differential privacy has progressed from theoretical to applied science in significant ways in the past few years. How is differential privacy being utilized? How does that help balance the trade-off between privacy and accuracy?

Roth: In the last five years or so, differential privacy has gone from an academic topic to a real technology. For example, the 2020 US Decennial Census is going to release all of its statistical products for the first time, subject to the protections of differential privacy. This is because, by law, the Census is required to protect the privacy of the people it is surveying. The ad hoc techniques used in previous decades to protect the statistics have been shown not to work.

I think that what we will see is that the statistics that the Census releases this year will be more protective of the privacy of Americans. However, in the theme of trade off, using rigorous privacy protections is not without cost. Certain kinds of analyses, such as detailed demographic studies that rely on having highly granular Census data, might now be unavailable under differential privacy. We've seen this play out in the public sphere between downstream users of the data and folks at Census who actually have to hammer out the details.

We've seen other interesting uses of differential privacy during the pandemic too. Some tech companies have utilized differential privacy when releasing statistics about personal mobility data gathered during the pandemic. What differential privacy is best at is releasing those kinds of population level statistics: It's exactly designed to prevent you from learning too much about any particular individual. If you want to know how much less people are moving around different cities because of coronavirus restrictions, these data sets let you answer that question without giving up too much privacy for individuals whose mobile devices were providing the data at the most granular level.

Q. So how does differential privacy help protect individual information?

Roth: Oftentimes the things that you will most naturally want to know about a data set are not facts about particular people, but are population level aggregates like, how many people are crowded into my supermarket at 6 a.m. when it opens. If you tell me sufficiently many aggregate statistics, I can do some math and back out particular people's data from that. The fact that aggregate statistics can be disclosive about individual people's data is an unfortunate accident that actually doesn't have too much to do with what you really wanted to learn.

At its most basic level, differential privacy does things like add little bits of noise to the statistics that you're releasing so that what you're telling me is not the exact number of people who were in my local supermarket at 6 a.m., but roughly the number of people who were in the supermarket plus or minus some small number of people. The fortunate mathematical fact is that you can add amounts of noise that are relatively small that still allow you to get good estimates, but are sufficient to wash out the contributions of particular people, making it impossible to learn too much about any particular individual. It lets you get access to these population level questions that you were curious about without incidentally or accidentally learning about particular people, which is the dangerous side.

"We are bullish about algorithms"
Michael Kearns and Aaron Roth talked to Oxford Academic about the future of AI.

Kearns: To make this slightly more concrete, say what I want to do is each day tell everybody how many people were in the supermarket a couple blocks from me at 1 p.m. If you happened to be at that supermarket at one o’clock, then your GPS data is one of the data points that goes into the count. You may consider your presence at supermarket at 1 p.m. to be the kind of private information that you don't want the whole world to know. So then let's say that, on a typical day, there might be a couple hundred people at the supermarket, but that I add a number which is an order of magnitude, plus or minus 25. The addition of that random number mathematically and provably obscures any individual’s contributions to that count. I won't be able to look at that count and try to figure out any particular person who was present. If I add a number that's between minus 25 and 25, I can't affect the overall count by 100. I'll still have an accurate count up to some resolution, but I will have provided privacy to everybody who was present at the supermarket and, actually, all the people who weren't present as well.

Q. How are topics like fairness, accountability, transparency, interpretability, and privacy showing up in computer science curriculum at Penn and elsewhere within higher education?

Kearns: When Aaron and I first started working on the technical aspects of fairness in machine learning and related topics, it was pretty sparsely populated. This was maybe six or seven years ago, and there weren't many papers on the topics. There were some older ones, more from the statistics literature, but there wasn't really a community of any size within machine learning that thought about these problems. On the research side, the opposite is now true. All of the major machine learning conferences have significant numbers of papers and workshops on these topics; they have workshops devoted to these topics. There are now standalone conferences about fairness, accountability, and explainability in machine learning that are growing every year. It's a very vibrant, active research community now. Additionally, even though it's still early, it's an important enough topic that there are now starting to be efforts to teach this even at the undergraduate level.

The last two years at Penn, for example, I have piloted a course called The Science of Data Ethics. It’s deliberately called that and not The Ethics of Data Science. What that represents is that it’s about the science of making algorithms that are more ethical by different norms, like fairness and privacy. It's not your typical engineering ethics course, which at some level is meant to teach you to be a good, responsible person in that you look at case studies where things went wrong and you talk about what you would do differently. This class is a science class. It says: Here are the standard principles of machine learning, here's how those standard principles can lead to discriminatory behavior in my predictive models, and here are alternate principles, or modifications of those principles and the algorithms that implement them, that avoid or mitigate that behavior.

Q. Is there a more multidisciplinary approach to this set of challenges?

Roth: It's definitely a multidisciplinary area. At Penn, we've been actively collaborating with interested folks in the law school and the criminology department. So far, we don't really have interdisciplinary undergraduate courses on these topics. Those courses would be good in the long run, but at the research and graduate level we've been having interdisciplinary conversations for a number of years.

In particular, one critique that we try to anticipate in the book, and that we’re very aware of, is that technical work on making algorithms more ethical is only one piece of a much larger sociological, or what some people would call socio-technical, pipeline.
Michael Kearns

Kearns: Not just at the teaching level, but even in the research community, there's a real melting pot of viewpoints on these topics. Even though our book is focused on the scientific aspects of these issues, we do spend some time mentioning the fact that the science will only take us so far. In particular, one critique that we try to anticipate in the book, and that we’re very aware of, is that technical work on making algorithms more ethical is only one piece of a much larger sociological, or what some people would call socio-technical, pipeline. Machine learning begins with data and ends with a model. But upstream from the data is the entire manner in which the data was collected and the conditions under which it was collected.

One of the things that's very interesting, exciting, and necessary about the dialogue around these kinds of issues is that, even when there's quite a bit to say on them scientifically, you don't want to just put your head down and look at the science. You want to talk to people who are upstream and downstream from the machine learning part of this pipeline because they bring very different perspectives, and can often point out perspectives which can help you change the way you look at things scientifically in a positive way.

Q. If I were a student exploring AI or ML and I wanted to influence this particular conversation, beyond technical skills, what kind of skills should I be developing?

Kearns: What I would very strongly advocate is: think widely, think broadly, think big. Yes, you're going to be doing technical work in particular models and frameworks, and you know you want to get results in those frameworks. But also read what people who are from very, very different fields think about these problems. Go to their conferences, don't just go to the machine learning conferences and to the sub-track on fairness and machine learning. Go to the interdisciplinary conferences and workshops that are deliberately meant to bring together scientists, legal scholars, philosophers, sociologists, and regulators. Hear their views on these problems, keep an ear out for whether they even think you're working on a problem that's relevant or even has a solution.

That's the way I have approached my career: focus on what I'm good at and what I think is interesting from a scientific standpoint, but not in a scientific vacuum. I deliberately expose myself whenever possible to what people from a completely different perspective are thinking about the same set of topics. The good news is that there's a lot of opportunity for that right now. If you work in some branch of material science, it may not be possible to wander out in the world and get diverse perspectives, but everybody has an opinion on AI and machine learning ethics these days, so there is no shortage of sources from which this hypothetical student could go out and find their own technical views challenged or broadened.

Roth: One trap that is very easy for a new PhD student, or even an established researcher, to fall into is to write the introductions to your papers motivated by some kind of fairness problem, but then find yourself solving some narrow technical problem that ultimately has little connection to the world. I am sometimes guilty of this myself, but this is an area where there really are lots of important problems to solve. It's an area where theoretical approaches, if wielded correctly, can be extremely valuable. The thing that’s valuable is to be, sort of, multilingual. It can be difficult to talk to people from other fields because those fields have different vocabularies and a different world view. However, it's important to understand the perspective of these different communities. There are interdisciplinary groups looking at fairness, accountability, and transparency, which bring people together from all sorts of backgrounds to actively work on developing, at the very least, a shared vocabulary—and hopefully a shared world view.

Q. You've become Amazon Scholars fairly recently. What inspired you to take on this role?

Roth: I've spent most of my career as a theorist, so the ways I've been primarily thinking about privacy and fairness are in the abstract. I've had fun thinking about questions like: What kinds of things are, and are not, possible in principle with differential privacy? Or what kinds of semantic fairness promises can you make to people in a way that is still consistent with trying to learn something from the data? The attraction of Amazon and AWS is that it's where the rubber meets the road. Here we are deploying real machine learning products, and the privacy and the fairness concerns are real and pressing.

My hope is that by having a foot in the practice of these problems, not just their theory, not only will I have some effect on how consequential products actually work, but I’ll learn things that will be helpful in developing new theory that is grounded in the real world.

Kearns: I've had a kind of second life in the quantitative finance industry up until I joined Amazon. While I spent time doing practical things in the world of finance, it was more just using my general knowledge in machine learning. The opportunity to come to Amazon and really think about the topics we've been discussing in a practical technological setting seemed like a great opportunity. I'm also a long-term fan and observer of the company. I’ve known people here for many years, and have had great conversations with them. So I’ve watched with great interest over the last decade plus as Amazon grew its machine learning effort from scratch and gradually grew it to have wider and wider applications. It’s now at a point where not only is machine learning used widely within the company to optimize all kinds of processes and recommendations and the like, but it’s also used by customers worldwide in the form of services like Amazon SageMaker.

I have watched this with great interest because when I was studying machine learning in graduate school back in the late 80s, trust me, it was an obscure corner of AI that people kind of raised their eyebrows at. I never would have thought we would reach the point where not only does The Wall Street Journal expect everyone to know what they mean when they write about machine learning, but that it would actually be a product sold at scale.

I've watched these developments from academia and from the world of finance.  It seemed like a great opportunity to combine my very specific current research and other interests with an inside look at one of the great technology companies. Like Aaron, my expectations, which were high, have only been exceeded in the time I've spent here.

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We’re looking for a passionate, talented, and inventive Applied Scientist to help build industry-leading technologies in language processing.Job responsibilitiesAs an Applied Scientist with the Alexa Artificial Intelligence (AI) team, you will be responsible for developing novel algorithms that advance the state-of-the-art in language processing and entity resolution, driving model and algorithmic improvements, formulating evaluation methodologies and for influencing design and architecture choices. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to build novel products and services that make use of speech and language technology. You will work in a hybrid, fast-paced organization where scientists and engineers work together and drive improvements to production. You will collaborate with and mentor other scientists to raise the bar of scientific research in Amazon.Amazon is committed to providing accommodations at all stages through recruitment and employment in accordance with applicable human rights and accommodation legislation. If contacted for an employment opportunity, please advise Human Resources if you require accommodation, including in order to apply for a position.
US, WA, Seattle
Do you want to join Alexa Artificial Intelligence (AI) - the science team behind Amazon’s intelligence voice assistance system? Do you want to utilize cutting-edge deep-learning and machine learning algorithms to delight millions of Alexa users around the world?If your answers to these questions are “yes”, then come join us with the Alexa AI team, which is in charge of improving Alexa user satisfaction through real-time metrics monitoring and continuous closed-loop learning. The team owns the modules that reduce user perceived defects and frictions through utterance reformulation, contextual and personalized hypothesis ranking.With the Alexa AI team, you will be working alongside a team of experienced machine/deep learning scientists and engineers to create data driven machine learning models and solutions on tasks such as sequence-to-sequence query reformulation, graph feature embedding, personalized ranking, etc..Job responsibilitiesYou will be expected to:· Analyze, understand, and model user-behavior and the user-experience based on large scale data, to detect key factors causing satisfaction and dissatisfaction (SAT/DSAT).· Build and measure novel online & offline metrics for personal digital assistants and user scenarios, on diverse devices and endpoints· Create and innovate deep learning and/or machine learning based algorithms for utterance reformulation and contextual hypothesis ranking to reduce user dissatisfaction in various scenarios;· Perform model/data analysis and monitor user-experienced based metrics through online A/B testing;· Research and implement novel machine learning and deep learning algorithms and modelsAmazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
US, WA, Seattle
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by preventing eCommerce fraud?Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems?Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you enjoy collaborating in a diverse team environment?If yes, then you may be a great fit to join the Amazon Buyer Risk Prevention (BRP) Machine Learning group. We are looking for a talented scientist who is passionate to build advanced algorithmic systems that help manage safety of millions of transactions every day.Major responsibilities· Use statistical and machine learning techniques to create scalable risk management systems· Learning and understanding large amounts of Amazon’s historical business data for specific instances of risk or broader risk trends· Design, development and evaluation of highly innovative models for risk management· Working closely with software engineering teams to drive real-time model implementations and new feature creations· Working closely with operations staff to optimize risk management operations,· Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation· Tracking general business activity and providing clear, compelling management reporting on a regular basis· Research and implement novel machine learning and statistical approachesPlease visit https://www.amazon.science for more informationAmazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
DE, BE, Berlin
Do you want to work as part of a team developing practical quantum technologies for AWS?Quantum technologies are rapidly emerging from the realms of science-fiction, and our customers can the see the potential they have to address their challenges. One of our missions at AWS is to give customers access to the most innovative technology available and help them continuously reinvent their business. Quantum technologies hold promise to be transformational in many industries, and we are adding quantum computing, networking and sensing resources to the toolkits of every researcher and developer.AWS is looking for Sr. Quantum Research Scientists to join an exceptional team of researchers and engineers. As a Sr. Research Scientist on the quantum networking team, you will:· Develop architectures, algorithms and applications for quantum networking.· Play a key role in shaping the development roadmap of the service, and evangelizing new features and capabilities· Have the opportunity to create narrative papers, write blogs, build demos and generate other reusable collateral that can be used by our customers· Establish thought leadership of AWS in the field by publishing research in journals and speaking at international scientific conferences· Most importantly, work closely with the broader AWS infrastructure teams to integrate quantum technologies and make them accessible to our customers· Mentor and develop more junior ScientistsThis role is based in the AWS Cambridge office but will involve collaboration internationally across the entire Amazon Quantum Computing organization.Our team is dedicated to supporting new team members. Our team has a broad mix of experience levels and Amazon tenures, and we’re building an environment that celebrates knowledge sharing and mentorship.Our team is intentional about attracting, developing, and retaining amazing talent from diverse backgrounds. We’re looking for a new teammate who is enthusiastic, empathetic, curious, motivated, reliable, and able to work effectively with a diverse team of peers; someone who will help us amplify the positive & inclusive team culture we’ve been building.
DE, BW, Tuebingen
Are you interested in working on fascinating scientific and engineering challenges of modern AI? Would you like to contribute to the development of the future generation of cloud computing at Amazon Web Services?As an Applied Scientist, you will be working on cutting edge research at the intersection of deep learning and causal inference. You will be part of an ambitious and multidisciplinary team of scientists and software engineers that is together developing novel tools to learn and exploit causal knowledge from real-world visual data.The AWS Causal Representation Learning Lab is located at the Tübingen site in Germany. Our goal is to develop the next generation of AI algorithms by learning and exploiting causal invariances extracted from non-i.i.d. visual data. Going beyond mere correlations, we quantify the causes of observations and provide robust predictions. Our mission is to provide credible and reliable AI models that do not fail unexpectedly under distribution shifts. The successful applicant will have previous research experience with either representation learning or causality, with an interest in the other.As an Applied Scientist in the Causal Representation Learning Lab, you will be responsible for:· Developing new machine learning and neural network architectures building on and inspired by causal principles· Causal discovery in complex environments and large-scale visual datasets· Benchmarks and data sets for causal representation learning· Developing evaluation pipelines to test model performance under distribution shifts· Engaging with product and development teams across AWS and Amazon to help bringing your scientific breakthroughs to customers· Contribute to our unique multidisciplinary environment with your own creativity and talentWe at AWS value individual expression, respect different opinions, and work together to create a culture where each of us is able to contribute fully. Our unique backgrounds and perspectives strengthen our ability to achieve Amazon's mission of being Earth's most customer-centric company.
GB, MLN, Edinburgh
We’re looking for a Machine Learning Scientist in the Personalization team for our Edinburgh office. You will be responsible for developing and disseminating customer-facing personalized recommendation algorithms. This is a hands-on role with global impact working with a team of world-class engineers and scientists across the Edinburgh offices and wider organization.You will design deep learning algorithms that scale to very large quantities of data, and serve high-scale low-latency recommendations to all customers worldwide. You will embody scientific rigor, designing and executing experiments to demonstrate the technical efficacy and business value of your methods. You will work alongside more senior team members to delight customers by aiding in recommendations relevancy, and raise the profile of Amazon as a global leader in machine learning and personalization.Successful candidates will have strong technical ability, focus on customers by applying a customer-first approach, excellent teamwork and communication skills, and a motivation to achieve results in a fast-paced environment. Our position offers exceptional opportunities for every candidate to grow their technical and non-technical skills. If you are selected, you have the opportunity to make a difference to our business by designing and building state of the art machine learning systems on big data, leveraging Amazon’s vast computing resources (AWS), working on exciting and challenging projects, and delivering meaningful results to customers world-wide.Key responsibilities· Develop deep learning algorithms for high-scale recommendations problem· Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative analysis and business judgement.· Collaborate with software engineering teams to integrate successful experimental results into large-scale, highly complex Amazon production systems capable of handling 100,000s of transactions per second at low latency.· Report results in a manner which is both statistically rigorous and compellingly relevant, exemplifying good scientific practice in a business environment.
US, NY, New York
Amazon Advertising is one of Amazon's fastest growing and most profitable businesses. As a core product offering within our advertising portfolio, Sponsored Products (SP) helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The SP team's primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day!The Ad Response Prediction team builds innovating click-through rate & conversation rate machine learning models, pipelines, and real-time serving infra to match shoppers’ intent to relevant ads on all devices, for all contexts and in all marketplaces. Through precise estimation of shoppers’ interaction with ads and their long-term value, we aim to drive optimal ads allocation and pricing, and help to deliver a relevant, engaging and delightful ads experience to Amazon shoppers. As the business and the complexity of various new initiatives we take continues to grow, we are looking for talented Applied Scientists to join the team.This team is directly impacting customers in meaningful ways and contributing to billions of dollars in revenue!As an Applied Scientist on this team you will:· Build machine learning models and perform data analysis to deliver scalable solutions to business problems.· Perform hands-on analysis and modeling with enormous data sets to develop insights that increase traffic monetization and merchandise sales without compromising shopper experience.· Work closely with software engineers on detailed requirements, technical designs and implementation of end-to-end solutions in production.· Design and run A/B experiments that affect hundreds of millions of customers, evaluate the impact of your optimizations and communicate your results to various business stakeholders.· Work with scientists and economists to model the interaction between organic sales and sponsored content and to further evolve Amazon's marketplace.· Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving.· Research new predictive learning approaches for the sponsored products business.· Write production code to bring models into production.Why you will love this opportunity: Amazon is investing heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate.Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding.Team video https://youtu.be/zD_6Lzw8raE
RO, Iasi
As an Research Scientist, you will participate in the design, development, and evaluation of models and machine learning (ML) technology to delight our customers. More specifically, as a member of the team, you will be involved in researching state ofthe art Computer Vision (CV) & ML solutions for Amazon devices and cloud services.About the hiring groupYou will join a team building the technology supporting our computer vision/machine learning research and development, that powers our key security alerting and rich notification services that help our customers protect their home. Our goal is to deliver the fastest, most reliable and flexible infrastructure that speeds up creation of smart features for security devices and that makes our customers feel “Always Home”
US, WA, Seattle
We’ve been hard at work developing a new, fully-electric delivery system – Amazon Scout – designed to get packages to customers using autonomous delivery devices. These devices were created by Amazon, are the size of a small cooler, and roll along sidewalks at a walking pace. We developed Amazon Scout at our research and development lab in Seattle, ensuring the devices can safely and efficiently navigate around pets, pedestrians and anything else in their path.The Amazon Scout team shares a passion for innovation using advanced technologies, a love of solving complex challenges, and a desire to impact customers in a meaningful way. We're looking for Scientists who like dealing with ambiguity, solving hard, large scale problems, and working in a startup like environment. To learn more about Amazon Scout, check out our Amazon Day One Blog post here: http://amazon.com/scoutAre you excited by using physics and electronics to help an autonomous robot sense its environment and operate safely? At Amazon Scout we are building a sidewalk-driving delivery robot. The Scout sensors team is tasked with providing the bot with advanced sensing ability ‘eyes and ears’ to generate the right type, quantity, quality and frequency of data to accomplish its mission.As an Applied Scientist specializing in sensors you will:· Lead early stage technical evaluation of different sensor concepts· Design, prototype and characterize novel sensor and systems.· Provide expertise on sensing and sensors capabilities and fundamentals.· Setup protocols and develop resources for evaluating and testing sensors.· Engage with and coordinate external vendors to customize and develop new sensing systems.· Assist in sensor functional integration with Scout hardware team.· Assist the simulation and application software teams in sensor data collection.Inclusive Team CultureHere at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have 12 affinity groups (employee resource groups) with more than 87,000 employees across hundreds of chapters around the world. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which reminds team members to seek diverse perspectives, learn and be curious, and earn trust.FlexibilityIt isn’t about which hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We offer flexibility and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthWe care about your career growth too. Whether your goals are to explore new technologies, take on bigger opportunities, or get to the next level, we'll help you get there. Our business is growing fast and our people will grow with it.
US, WA, Redmond
Project Kuiper is an initiative to launch a constellation of Low Earth Orbit satellites that will provide low-latency, high-speed broadband connectivity to underserved communities around the world. This position is part of the Satellite Attitude Determination and Control team. You will design and analyze the control system and algorithms, support development of our flight hardware and software, help integrate the satellite in our labs, participate in flight operations, and see a constellation of satellites flow through the production line in the building next door.A day in the lifeThis is an opportunity to play a significant role early in the design of an entirely new satellite system with challenging performance requirements. The large, integrated constellation brings opportunities for advanced capabilities that need investigation and development. The constellation size also puts emphasis on engineering excellence so our tools and methods, from conceptualization through manufacturing and all phases of test, will be state of the art as will the satellite and supporting infrastructure on the ground.About the hiring groupOur team has lots of experience with various satellite systems and many other flight vehicles. We have bench strength in both our mission and core GNC disciplines. We design, prototype, test, iterate and learn together. Because GNC is central to safe flight, we tend to drive Concepts of Operation and many system level analyses.You will find that Kuiper's mission is compelling, so our program is staffed with some of the top engineers in the industry. Our daily collaboration with other teams on the program brings constant opportunity for discovery, learning, and growth.Job responsibilitiesYou will design and analyze algorithms for estimation, flight control, and precise pointing using linear methods and simulation. Along with that will be plenty of work developing and applying models and simulations, with various levels of fidelity, of the satellite and our constellation. The labs in our building provide for component level environmental testing, functional and performance checkout, subsystem integration, and satellite integration up to Day in the Life testing and mission rehearsal. We are excited to get into space, so you can look forward to seeing your designs launch and fly soon!Export Control Requirement:Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum.Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
US, WA, Seattle
Are you excited at the prospect of conducting research to improve the employee and manager experience at Amazon? Would you like to see your findings have a real impact on key HR programs and processes? Join the People Analytics, Insights, and Research team that supports three of Amazon’s most exciting businesses: Devices & Services (the innovator behind Alexa, Kindle, and Echo), Global Media and Entertainment (the business that produces TV shows, new gaming technologies, and runs Amazon music), and Advertising (one of the fastest growing businesses at Amazon).We are seeking a Senior Research Scientist with expertise in mixed-methods research, preferably in social science and behavioral research, but are also open to experience in public health, economics, or similar fields. In this role, you will lead and support research efforts within the Recruiting, Talent Management, and Leadership & Development space.You will help set the direction for science and research in the organization. You will be a thought leader on the team, partnering with a diverse set of stakeholders to identify and develop impactful areas for novel research about talent and recruiting outcomes, mechanisms, and programs. You will mentor and provide scientific expertise/peer review to other scientists and analysts on the team.The ideal candidate should have strong problem solving skills, excellent business acumen, as well as an expertise in both qualitative and quantitative methods. This role will need to navigate complex and ambiguous business challenges by asking the right questions, understanding what methodologies to employ, and communicating results to multiple audiences (e.g., technical peers, functional teams, business leaders). Translating business and stakeholder needs into realistic and actionable scientific research will be a regular challenge in this role.Responsibilities include:· Leading scientific direction, providing consult, mentorship, and peer review.· Partnering closely and driving effective collaborations across multi-disciplinary science, analytics, HR, and business teams. Reviewing and scoping research requests, and recommending appropriate scientific methodologies.· Designing, developing, and executing qualitative and quantitative data collection, research, and experimentation. Strong experience with statistical analysis required. Experience with survey development and experimental design (including quasi-experimental) preferred.· Communicating findings and business impact effectively (written and verbally) with both technical and non-technical stakeholders.
US, CA, Sunnyvale
Interested in Amazon Alexa? We’re building the speech and language solutions behind Amazon products and services. Come join us!As a Senior Applied Scientist in Alexa AI, you will perform the duties of a research scientist and are also expected to be strong at implementing the algorithm. You are expected be an expert in machine learning approaches for conversational language and speech processing. Our mission is to innovate the state-of-the-art core technologies in these areas.Job responsibilities· Research, design, implement, analyze and evaluate novel algorithms for multimodal conversational AI using deep learning· Develop state-of-the-art algorithms, contribute to Amazon's Intellectual Property and publish at top-tier conferences· Collaborate closely with team members on developing algorithms and demonstrating them in systems including prototypes and productsAmazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
US, WA, Seattle
We are a team of doers working passionately to apply cutting-edge advances in diagnostics technology to solve real-world problems. As a Senior Immunoassay Scientist, you will work with a unique and gifted team developing exciting medical diagnostic products and collaborate with cross-functional teams. Our team rewards intellectual curiosity while maintaining a laser-focus in bringing products to market. Competitive candidates are responsive, flexible, and able to succeed within an open, collaborative, entrepreneurial, startup-like environment. At the cutting edge of both academic and applied research in this product area, you have the opportunity to work together with some of the most talented scientists, engineers, and product managers.Inclusive Team Culture:Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.The Senior Immunoassay Scientist should be an expert in using immunoassays to develop assays leading to commercially viable products. They should have a deep understanding of underlying biochemical processes and components within conventional immunoassay platforms, be someone capable of diving deep into the data (requires in-depth understanding of the lab processes, for example to do root cause analysis of experimental outcome based on the data), and be someone who can work independently.Job responsibilities· Research and develop both existing and new cutting edge immunoassay technologies· Carefully execute laboratory experiments and provide guidance to teams of scientists and engineers through the writing of clear standard operating protocols.· Assist with assay process transfer to production environments and design and execute verification/validation plans· Work with a team of scientists and engineers to communicate experiments and conclusion for review· Work closely with clinical, regulatory and quality stakeholders· Follow literature in the field