Card-Imbens 16x9.jpg
David Card (left), an Amazon Scholar, a professor of economics at the University of California, Berkeley, and the outgoing president of the AEA, and Guido Imbens (right), an academic research consultant at Amazon and a professor at the Stanford Graduate School of Business.

A conversation with economics Nobelists

Amazon Scholar David Card and academic research consultant Guido Imbens on the past and future of empirical economics.

The annual meeting of the American Economic Association (AEA) took place Jan. 7 - 9, and as it approached, Amazon Science had the chance to interview two of the three recipients of the 2021 Nobel Prize in economics — who also happen to be Amazon-affiliated economists.

David Card, an Amazon Scholar, a professor of economics at the University of California, Berkeley, and the outgoing president of the AEA, won half the prize “for his empirical contributions to labor economics”.

Guido Imbens, an academic research consultant at Amazon and a professor at the Stanford Graduate School of Business, shared the other half of the prize with MIT’s Josh Angrist for “methodological contributions to the analysis of causal relationships”.

Amazon Science: The empirical approach to economics has been recognized by the Nobel Prize committee several times in the last few years, but it wasn't always as popular as it is today. I'm curious how you both first became interested in empirical approaches to economics.

David Card: The heroes of economics for many, many decades were the theorists, and in the postwar era especially, there was a recognition that economic modeling was underdeveloped — the math was underdeveloped — and there was a need to formalize things and understand better what the models really delivered.

People started to realize that we had the data to better look at real labor market phenomena and possibly make economics something different than just a kind of a branch of philosophy.
David Card

That need really proceeded through the ’60s, and Arrow and Debreu were these famous mathematical economists who developed some very elegant theoretical models of how the market works in an idealized economy.

What happened in my time was people started to realize that we had the data to better look at real labor market phenomena and possibly make economics something different than just a kind of a branch of philosophy. Arrow-Debreu is basically mathematical philosophy.

Guido Imbens: I came from a very different tradition. I grew up in the Netherlands, and there was a strong tradition of econometrics started by people like Tinbergen. Tinbergen had been very broad — he did econometrics, but he also did empirical work and was very heavily involved in policy analysis. But over time, the program he had started was becoming much more focused on technical econometrics.

So as an undergraduate, we didn't really do any empirical work. We really just did a lot of mathematical statistics and some operations research and some economic theory. My thesis was a theoretical econometrics study.

When I presented that at Harvard, Josh Angrist wasn't really all that impressed with it, and he actually opposed the department hiring me there because he thought the paper was boring. And he was probably right! But luckily, the more senior people there at the time thought I was at least somewhat promising. And so I got hired at Harvard. But then it was really Josh and Larry Katz, one of the labor economists there, who got me interested in going to the labor seminar and got me exposed to the modern empirical work.

The context Josh and I started talking in really was this paper that I think came up in all three of the Nobel lectures, this paper by Ed Leamer, “Let's Take the Con Out of Econometrics”, where Leamer says, “Hardly anyone takes data analysis seriously. Or perhaps more accurately, hardly anyone takes anyone else’s data analysis seriously.”

And I think Leamer was right: people did these very elaborate things, and it was all showing off complicated technical things, but it wasn't really very credible. In fact, Leamer presented a lecture based on that work at Harvard. And I remember Josh getting up at some point and saying, “Well, you talk about all this old stuff, but look at the work Card does. Look at the work Krueger does. Look at the work I do. It's very different.”

And that felt right to me. It felt that the work was qualitatively very different from the work that Ed Leamer was describing and that he was complaining about.

AS: So that's when you first became aware of Professor Card’s work. Professor Card, when did you first become aware of Professor Imbens’s work?

Card: One of his early papers was pretty interesting. He was trying to combine data from micro survey evidence with benchmark numbers that you would get from a population, and it's actually a version of a kind of a problem that arises at Amazon all the time, which is, we've got noisy estimates of something, and we've got probably reliable estimates of some other aggregates, and there's often ways to try and combine those. I saw that and I thought that was very interesting.

Then there’s the problem that Josh and Guido worked on that was most impactful and that was cited by the Nobel Prize committee. I had worked on an experiment, a real experiment [as opposed to a natural experiment], in welfare analysis in Canada, and it was providing an economic incentive to try and get single mothers off of welfare and into work. And we noticed that the group of mothers who complied or followed on with the experiment was reasonable size, but it wasn't 100%.

We did some analysis of it trying to characterize them. Around the same time, I became aware of Imbens’s and Angrist’s paper, which basically formalized that a lot better and described what exactly was going on with this group. That framework just instantly took off, and everyone within a few years was thinking about problems that way.

This morning I was talking to another Amazon person about a problem. It was a difference analysis. I was saying we should try and characterize the compliers for this difference intervention. So it's exactly this problem.

The Nobel committee’s press release for Card, Imbens, and Angrist’s prize announcement emphasizes their use of natural experiments, which it defines as “situations in which chance events or policy changes result in groups of people being treated differently, in a way that resembles clinical trials in medicine.” A seminal instance of this was Card’s 1993 paper with his Princeton colleague Alan Krueger, which compared fast-food restaurants in two demographically similar communities on either side of the New Jersey-Pennsylvania border, one of which had recently seen a minimum-wage hike and one of which hadn’t.

AS: In the early days, there was skepticism about the empirical approach to economics. So every time you selected a new research project, you weren't just trying to answer an economics problem; you were also, in a sense, establishing the credibility of the approach. How did you select problems then? Was there a structure that you recognized as possibly lending itself to natural experiment?

Card: I think that the natural-experiment thing — there was really a brief period where that was novel, to tell you the truth. Maybe 1989 to 1992 or 3. I did this paper on the Mariel boatlift, which was cited by the committee. But to tell you the truth, that was a very modest paper. I never presented it anywhere, and it's in a very modest journal. So I never thought of that paper as going anywhere [laughs].

What happened was, it became more and more well understood that in order to make a claim of causality even from a natural-experiment setting, you had to have a fair amount of information from before the experiment took place to validate or verify that the group that you were calling the treatment group and the group that you were calling the control group actually were behaving the same.

That was a weakness of the project that Alan Krueger and I did. We had restaurants in New Jersey and Pennsylvania. We knew the minimum wage was going to increase — or we thought we knew that; it wasn't entirely clear at the time — but we surveyed the restaurants before, and then the minimum wage went up, and we surveyed them after, and that was good.

But we didn't really have multiple surveys from before to show that in the absence of the minimum wage, New Jersey and Pennsylvania restaurants had tracked each other for a long time. And these days, that's better understood. At Amazon for instance, people are doing intervention analyses of this type. They would normally look at what they call pre-trend analysis, make sure that the treatment group and the control group are trending the same beforehand.

I think there are 1,000 questions in economics that have been open forever. Sometimes new datasets come along. That's been happening a lot in labor economics: huge administrative datasets have become available, richer and richer, and now we're getting datasets that are created by these tech firms. So my usual thing is, I think, that's a dataset that maybe we can answer this old question on. That’s more my approach.

That's why being at Amazon has been great .... A lot of people have substantive questions they're trying to analyze with data, and they're kind of stuck in places, so there's a need for new methodologies.
Guido Imbens

Imbens: I come from a slightly different perspective. Most of my work has come from listening to people like David and Josh and seeing what type of problems they're working on, what type of methods they're using, and seeing if there's something to be added there — if there’s some way of improving the methods or places where maybe they're stuck, but listening to the people actually doing the empirical work rather than starting with the substantive questions.

That's why being at Amazon has been great, from my perspective. A lot of people have substantive questions they're trying to analyze with data, and they're kind of stuck in places, so there's a need for new methodologies. It's been a very fertile environment for me to come up with new research.

AS: Methodologically, what are some of the outstanding questions that interest you both?

Imbens: Well, one of the things is experimental design in complex environments. A lot of the experimental designs we’re using at the moment still come fairly directly from biomedical settings. We have a population, we randomize them into a treatment group and a control group, and then we compare outcomes for the two groups.

But in a lot of the settings we’re interested in at Amazon, there are very complex interactions between the units and their experiences, and dealing with that is very challenging. There are lots of special cases where we know somewhat what to do, but there are lots of cases where we don't know exactly what to do, and we need to do more complex experiments to get the answers to the questions we're interested in.

Double randomization — original color scheme.jpeg
An example of what Imbens calls “experimental design in complex environments”. In this illustration, each of five viewers is shown promotions for eight different Prime Video shows. Some of those promotions contain extra information, indicated in the image by star ratings (the “treatment”). This design helps determine whether the treatment affects viewing habits (the viewer experiment) but also helps identify spillover effects, in which participation in the viewer experiment influences the viewer’s behavior in other contexts.

The second thing is, we do a lot of these experiments, but often the experiments are relatively small. They’re small in duration, and they’re small in size relative to the overall population. You know, it goes back to the paper we mentioned before, combining this observational-study data with experimental data. That raises a lot of interesting methodological challenges that I spend a lot of time thinking about these days.

AS: I wondered if in the same way that in that early paper you were looking at survey data and population data, there's a way that natural experiments and economic field experiments can reinforce each other or give you a more reliable signal than you can get from either alone.

Card: There's one thing that people do; I've done a few of these myself. It's called meta analysis. It's a technique where you take results from different studies and try and put them into a statistical model. In a way it's comparable to work Guido has done at Amazon, where you take a series of actual experiments, A/B experiments done in Weblab, and basically combine them and say, “Okay, these aren't exactly the same products and the same conditions, but there's enough comparability that maybe I can build a model and use the information from the whole set to help inform what we're learning from any given one.”

And you can do that in studies in economics. For example, I’ve done one on training programs. There are many of these training programs. Each of them — exactly as Guido was saying — is often quite small. And there are weird conditions: sometimes it's only young males or young females that are in the experiment, or they don't have very long follow-up, or sometimes the labor market is really strong, and other times it's really weak. So you can try and build a model of the outcome you get from any given study and then try and see if there are any systematic patterns there.

Imbens: We do all these experiments, but often we kind of do them once, and then we put them aside. There's a lot of information over the years built up in all these experiments we've done, and finding more of these meta-analysis-type ways of combining them and exploiting all the information we have collected there — I think it's a very promising way to go.

AS: How can empirical methods complement theoretical approaches — model building of the kind that, in some sense, the early empirical research was reacting against?

Card: Normally, if you're building a model, there are a few key parameters, like you need to get some kind of an elasticity of what a customer will do if faced with a higher price or if offered a shorter, faster delivery speed versus slower delivery speed. And if you have those elasticities, then you can start building up a model.

If you have even a fairly complicated dynamic model, normally there's a relatively small number of these parameters, and the value of the model is to take this set of parameters and try and tell a bit richer story — not just how the customer responds to an offer of a faster delivery today but how that affects their future purchases and whether they come back and buy other products or whatever. But you need credible estimates of those elasticities. It's not helpful to build a model and then just pull numbers out of the air [laughs]. And that's why A/B experiments are so important at Amazon.

AS: I asked about outstanding methodological questions that you're interested in, but how about economic questions more broadly that you think could really benefit from an empirical approach?

Card: In my field [labor economics], we've begun to realize that different firms are setting different wages for the same kinds of workers. And we're starting to think about two issues related to that. One is, how do workers choose between jobs? Do they know about all the jobs out there? Do they just find out about some of the jobs? We're trying to figure out exactly why it's okay in the labor market for there to be multiple wages for a certain class of workers. Why don't all the workers immediately try to go to one job? This seems to be a very important phenomenon.

And on the other side of that, how do employers think about it? What are the benefits to employers of a higher wage or lower wage? Is it just the recruiting, or is it retention, or is it productivity? Is it longer-term goals? That's front and center in the research that I do outside of Amazon.

AS: I was curious if there were any cases where a problem presented itself, and at first you didn't think there was any way to get an empirical handle on it, and then you figured out that there was.

We're supposed to be social scientists who are trying to see what people are doing and the problems they confront and trying to analyze them. ... That's different than this old-fashioned Adam Smith view of the economy as a perfectly functioning tool that we're just supposed to admire.
David Card

Card: I saw a really interesting paper that was done by a PhD student who was visiting my center at Berkeley. In European football, there are a lot of non-white players, and fan racism is pretty pervasive. This guy noticed that during COVID, they played a lot of games with no fans. So he was able to compare the performance of the non-white and white players in the pre-COVID era and the COVID era, with and without fans, and showed that the non-white players did a little bit better. That's the kind of question where you’re saying, How are we ever going to study that? But if you're thinking and looking around, there's always some angle that might be useful.

Imbens: That's a very clever idea. I agree with David. If you just pay attention, there are a lot of things happening that allow you to answer important questions. Maybe fan insults in sports itself isn't that big a deal, but clearly, racism in the labor market and having people treated differently is a big problem. And here you get a very clear handle on an aspect of it. And once you show it's a problem there, it's very likely that it shows up in arguably substantively much more important settings where it's really hard to study.

In the Netherlands for a long time, they had a limit on the number of students who could go to medical school. And it wasn't decided by the medical schools themselves; they couldn't choose whom to admit. It was partly based on a lottery. At some point, someone used that to figure out how much access to medical school is actually worth. So essentially, you have two people who are both qualified to go to medical school; one gets lucky in the lottery; one doesn't. And it turns out you're giving the person who wins the lottery basically a lot of money. Obviously, in many professions we can't just randomly assign people to different types of jobs. But here you get a handle on the value of rationing that type of education.

Card: I think that's really important. You know, we're supposed to be social scientists who are trying to see what people are doing and the problems they confront and trying to analyze them. In a way, that's different than this sort of old-fashioned Adam Smith view of the economy as a perfectly functioning tool that we're just supposed to admire. That is a difference, I think.

Research areas

Related content

US, CA, Santa Clara
Job summaryAmazon is looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help build industry-leading language technology.Our mission is to provide a delightful experience to Amazon’s customers by pushing the envelope in Natural Language Processing (NLP), Natural Language Understanding (NLU), Dialog management, conversational AI and Machine Learning (ML).As part of our AI team in Amazon AWS, you will work alongside internationally recognized experts to develop novel algorithms and modeling techniques to advance the state-of-the-art in human language technology. Your work will directly impact millions of our customers in the form of products and services, as well as contributing to the wider research community. You will gain hands on experience with Amazon’s heterogeneous text and structured data sources, and large-scale computing resources to accelerate advances in language understanding.We are hiring primarily in Conversational AI / Dialog System Development areas: NLP, NLU, Dialog Management, NLG.This role can be based in NYC, Seattle or Palo Alto.Inclusive Team CultureHere at AWS, 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.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 flow 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.
IN, TS, Hyderabad
Job summaryAre you excited about driving business growth for millions of sellers by applying Machine Learning? Do you thrive in a fast-moving, large-scale environment that values data-driven decision making and sound scientific practices? We are looking for experienced data scientists to build sophisticated decision making systems that help Amazon Marketplace Sellers to grow their businesses.Amazon Marketplace enables sellers to reach hundreds of millions of customers and provides sellers the tools and services needed to make e-commerce simple, efficient and successful. Our team builds the core intelligence, insights, and algorithms that power a range of products used by millions of sellers. We are tackling large-scale, challenging problems such as helping sellers to prioritise business tasks by bringing together petabytes of data from sources across Amazon.You will be proficient with creating value out of data by formulating questions, analysing vast amounts of data, and communicating insights effectively to audience of varied backgrounds. In addition, you'll contribute to online experiments, build machine learning pipelines and personalised data products.To know more about Amazon science, Please visit https://www.amazon.scienceKey job responsibilities· Collaborate with domain experts, formulate questions, gather, process and analyse petabytes of data to unearth reliable insights· Design & execute experiments and analyze experimental results· Communicate insights effectively to audience of a wide range of backgrounds· Formulate relevant prediction problems and solve them by developing machine learning models· Partner with data engineering teams to improve quality of data assets, metrics and insights· Leverage industry best practices to establish repeatable science practices, principles & processes
US, WA, Seattle
Job summaryAmazon Sub-Same-Day Supply Chain team is looking for an experienced and motivated Senior Data Scientist to generate data-driven insights influencing the long term SSD supply chain strategy, build the necessary predictive models, optimization algorithms and customer behavioral segments allowing us to discover and build the roadmap for SSD to enable operational efficiency and scale.Key job responsibilitiesWork with product managers, engineers, other scientists, and leadership to identify and prioritize complex problems.Translate business problems into specific analytical questions and form hypotheses that can be answered with available data using scientific methods or identify additional data needed in the master datasets to fill any gapsDesign, develop, and evaluate highly innovative statistics and ML modelsGuide and establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementationProactively seek to identify business opportunities and insights and provide solutions to shape key business processes and policies based on a broad and deep knowledge of Amazon data, industry best-practices, and work done by other teams.A day in the lifeIn this role, you will be a technical expert with significant scope and impact. You will work with Product Managers, Business Engineers, and other Scientists, to deeply understand SSDs current optimization strategy while benchmarking against industry best practices and standards to gain insights that will drive our roadmap. A successful Data Scientist will have extreme bias for action needed in a startup environment, with outstanding leadership skills, proven ability to build and manage medium-scale modeling projects, identify data requirements, build methodology and tools that are statistically grounded. It will be a person who enjoys diving deep into data, doing analysis, discovering root causes, and designing long-term scientific solutions. We are seeking someone who can thrive in a fast-paced, high-energy and fun work environment where we deliver value incrementally and frequently. We value highly technical people who know their subject matter deeply and are willing to learn new areas. We look for individuals who know how to deliver results and show a desire to develop themselves, their colleagues, and their career.About the teamAmazon's Sub-Same Day (SSD) delivery program is designed to get customers their items as fast as possible – currently in as quickly as five hours. With ultra-fast delivery becoming increasingly important, we are looking for an experienced Senior Data Scientist to help us benchmark against industry standards to uncover insights to improve and optimize the long term supply chain strategy for Amazons Sub-Same-Day business.
US, NY, New York
Job summaryJob summaryAmazon is looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help build industry-leading language technology.Our mission is to provide a delightful experience to Amazon’s customers by pushing the envelope in Natural Language Processing (NLP), Natural Language Understanding (NLU), Dialog management, conversational AI and Machine Learning (ML).As part of our AI team in Amazon AWS, you will work alongside internationally recognized experts to develop novel algorithms and modeling techniques to advance the state-of-the-art in human language technology. Your work will directly impact millions of our customers in the form of products and services, as well as contributing to the wider research community. You will gain hands on experience with Amazon’s heterogeneous text and structured data sources, and large-scale computing resources to accelerate advances in language understanding.We are hiring primarily in Conversational AI / Dialog System Development areas: NLP, NLU, Dialog Management, NLG.This role can be based in NYC, Seattle or Palo Alto.Inclusive Team CultureHere at AWS, 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.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 flow 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.
US, VA, Arlington
Job summaryAmazon is investing heavily in building a world class advertising business and we are responsible for defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities.Sponsored Products helps merchants, retail vendors, and brand owners succeed via native advertising that grows incremental sales of their products sold through Amazon. The Sponsored Products Ad Marketplace organization optimizes the systems and ad placements to match advertiser demand with publisher supply using a combination of machine learning, big data analytics, ultra-low latency high-volume engineering systems, and quantitative product focus. Our goals are to help buyers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and to build a major, sustainable business that helps Amazon continuously innovate on behalf of all customers.We are seeking a Sr. Applied Science Manager who has a solid background in applied Machine Learning and AI, deep passion for building data-driven products, ability to communicate data insights and scientific vision, and has a proven track record of leading both applied scientists and software engineers to execute complex projects and deliver business impacts.In this team, Machine Learning and Deep Learning technologies including Semantic Retrieval, Natural Language Processing (NLP), Information Extraction, Image Understanding, Learning to Rank are used to match shoppers' search queries to ads with per impression prediction models that run in real-time with tight latency budgets. Models are trained using self-supervised techniques, transfer learning, and supervised training using labeled datasets. Knowledge distillation and model compression techniques are used to optimize model performance for production serving.The Senior Manager role will lead science and engineering efforts in these areas for Amazon Search pages WW. The person in this role is responsible for: maintaining the consistent and long term reliability for the models and the delivery services that power them, managing diverse teams across multiple domains, and collaborating cross-functional with other senior decision makers. Our critical LPs for this role are Think Big, Are Right A lot, and Earns Trust. What is key is that the leader will need a dynamic mindset to build systems that are flexible and will scale.In this role, you will:· Lead a group of both applied scientists and software engineers to deliver machine-learning and AI solutions to production.· Advance team's engineering craftsmanship and drive continued scientific innovation as a thought leader and practitioner.· Develop science and engineering roadmap, run Sprint/quarter and annual planning, and foster cross-team collaboration to execute complex projects.· Perform hands-on data analysis, build machine-learning models, run regular A/B tests, and communicate the impact to senior management.· Hire and develop top talents, provide technical and career development guidance to both scientists and engineers in the organization.Locations: Seattle, WA; New York, NY; Arlington, VA
US, WA, Seattle
Job summaryWorkforce Staffing (WFS) brings together the workforce powering Amazon’s ability to delight customers: the Amazon Associate. With over 1M hires, WFS supports sourcing, hiring, and developing the best talent to work in our fulfillment centers, sortation centers, delivery stations, shopping sites, Prime Air locations, and more.WFS' Funnel Science and Analytics team is looking for a Research Scientist. This individual will be responsible for conducting experiments and evaluating the impact of interventions when conducting experiments is not feasible. The perfect candidate will have the applied experience and the theoretical knowledge of policy evaluation and conducting field studies.Key job responsibilitiesAs a Research Scientist (RS), you will do causal inference, design studies and experiments, leverage data science workflows, build predictive models, conduct simulations, create visualizations, and influence science and analytics practice across the organization.Provide insights by analyzing historical data from databases (Redshift, SQL Server, Oracle DW, and Salesforce).Identify useful research avenues for increasing candidate conversion, test, and create well written documents to communicate to technical and non-technical audiences.About the teamFunnel Science and Analytics team finds ways to maximize the conversion and early retention of every candidate who wants to be an Amazon Associate. By focusing on our candidates, we improve candidate and business outcomes, and Amazon takes a step closer to being Earth’s Best Employer.
US, NY, New York
Job summaryAmazon is looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help build industry-leading language technology.Our mission is to provide a delightful experience to Amazon’s customers by pushing the envelope in Natural Language Processing (NLP), Natural Language Understanding (NLU), Dialog management, conversational AI and Machine Learning (ML).As part of our AI team in Amazon AWS, you will work alongside internationally recognized experts to develop novel algorithms and modeling techniques to advance the state-of-the-art in human language technology. Your work will directly impact millions of our customers in the form of products and services, as well as contributing to the wider research community. You will gain hands on experience with Amazon’s heterogeneous text and structured data sources, and large-scale computing resources to accelerate advances in language understanding.We are hiring primarily in Conversational AI / Dialog System Development areas: NLP, NLU, Dialog Management, NLG.This role can be based in NYC, Seattle or Palo Alto.Inclusive Team CultureHere at AWS, 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.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 flow 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.
US, WA, Seattle
Job summaryAmazon's Weblab team enables experimentation at massive scale to help Amazon build better products for customers. A/B testing is in Amazon's DNA and we're at the core of how Amazon innovates on behalf of customers. We are seeking a skilled Applied Scientist to help us build the future of experimentation systems at Amazon.About you:You have an entrepreneurial spirit and want to make a big impact on Amazon and its customers. You are excited about cutting-edge research on unsupervised learning, graph algorithms, and causal inference in the intersection between Machine Learning, Statistics, and Econometrics. You enjoy building massive scale and high performance systems but also have a bias for delivering simple solutions to complex problems. You're looking for a career where you'll be able to build, to deliver, and to impress. You challenge yourself and others to come up with better solutions. You develop strong working relationships and thrive in a collaborative team environment.About us together:We're going to help Amazon make better long term decisions by designing and delivering A/B-testing systems for long-term experiments, and by using these systems to figure out how near term behavior impacts long term growth and profitability. Our work will inform some of the biggest decisions at Amazon. Along the way, we're going to face seemingly insurmountable challenges. We're going to argue about how to solve them, and we'll work together to find a solution that is better than each of the proposals we came in with. We'll make tough decisions, but we'll all understand why. We'll be the dream team.We have decades of combined experience on the team in many areas science and engineering so it's a great environment in which to learn and grow. A/B testing is one of the hottest areas of research and development in the world today and this is a chance to learn how it works in the company known for pioneering its use.
US, CA, Santa Clara
Job summaryAWS AI/ML is looking for world class scientists and engineers to join its AI Research and Education group working on building automated ML solutions for planetary-scale sustainability and geospatial applications. Our team's mission is to develop ready-to-use and automated solutions that solve important sustainability and geospatial problems. We live in a time wherein geospatial data, such as climate, agricultural crop yield, weather, landcover, etc., has become ubiquitous. Cloud computing has made it easy to gather and process the data that describes the earth system and are generated by satellites, mobile devices, and IoT devices. Our vision is to bring the best ML/AI algorithms to solve practical environmental and sustainability-related R&D problems at scale. Building these solutions require a solid foundation in machine learning infrastructure and deep learning technologies. The team specializes in developing popular open source software libraries like AutoGluon, GluonCV, GluonNLP, DGL, Apache/MXNet (incubating). Our strategy is to bring the best of ML based automation to the geospatial and sustainability area.We are seeking an experienced Applied Scientist for the team. This is a role that combines science knowledge (around machine learning, computer vision, earth science), technical strength, and product focus. It will be your job to develop ML system and solutions and work closely with the engineering team to ship them to our customers. You will interact closely with our customers and with the academic and research communities. You will be at the heart of a growing and exciting focus area for AWS and work with other acclaimed engineers and world famous scientists. You are also expected to work closely with other applied scientists and demonstrate Amazon Leadership Principles (https://www.amazon.jobs/en/principles).Strong technical skills and experience with machine learning and computer vision are required. Experience working with earth science, mapping, and geospatial data is a plus. Our customers are extremely technical and the solutions we build for them are strongly coupled to technical feasibility.About the teamInclusive Team CultureAt AWS, 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 flow 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. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded scientist and enable them to take on more complex tasks in the future.Interested in this role? Reach out to the recruiting team with questions or apply directly via amazon.jobs.
US, CA, Santa Clara
Job summaryAWS AI/ML is looking for world class scientists and engineers to join its AI Research and Education group working on building automated ML solutions for planetary-scale sustainability and geospatial applications. Our team's mission is to develop ready-to-use and automated solutions that solve important sustainability and geospatial problems. We live in a time wherein geospatial data, such as climate, agricultural crop yield, weather, landcover, etc., has become ubiquitous. Cloud computing has made it easy to gather and process the data that describes the earth system and are generated by satellites, mobile devices, and IoT devices. Our vision is to bring the best ML/AI algorithms to solve practical environmental and sustainability-related R&D problems at scale. Building these solutions require a solid foundation in machine learning infrastructure and deep learning technologies. The team specializes in developing popular open source software libraries like AutoGluon, GluonCV, GluonNLP, DGL, Apache/MXNet (incubating). Our strategy is to bring the best of ML based automation to the geospatial and sustainability area.We are seeking an experienced Applied Scientist for the team. This is a role that combines science knowledge (around machine learning, computer vision, earth science), technical strength, and product focus. It will be your job to develop ML system and solutions and work closely with the engineering team to ship them to our customers. You will interact closely with our customers and with the academic and research communities. You will be at the heart of a growing and exciting focus area for AWS and work with other acclaimed engineers and world famous scientists. You are also expected to work closely with other applied scientists and demonstrate Amazon Leadership Principles (https://www.amazon.jobs/en/principles).Strong technical skills and experience with machine learning and computer vision are required. Experience working with earth science, mapping, and geospatial data is a plus. Our customers are extremely technical and the solutions we build for them are strongly coupled to technical feasibility.About the teamInclusive Team CultureAt AWS, 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 flow 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. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded scientist and enable them to take on more complex tasks in the future.Interested in this role? Reach out to the recruiting team with questions or apply directly via amazon.jobs.