Michael I. Jordan, Amazon scholar and professor at the University of California, Berkeley
Michael I. Jordan, Amazon scholar and professor at the University of California, Berkeley
Credit: Flavia Loreto

Artificial Intelligence—The revolution hasn’t happened yet

Michael I. Jordan, Amazon scholar and professor at the University of California, Berkeley, writes about the classical goals in human-imitative AI, and reflects on how in the current hubbub over the AI revolution it is easy to forget that these goals haven’t yet been achieved. This article is reprinted with permission from the Harvard Data Science Review, where it first appeared.

Artificial Intelligence (AI) is the mantra of the current era. The phrase is intoned by technologists, academicians, journalists, and venture capitalists alike. As with many phrases that cross over from technical academic fields into general circulation, there is significant misunderstanding accompanying use of the phrase. However, this is not the classical case of the public not understanding the scientists—here the scientists are often as befuddled as the public. The idea that our era is somehow seeing the emergence of an intelligence in silicon that rivals our own entertains all of us, enthralling us and frightening us in equal measure. And, unfortunately, it distracts us.

There is a different narrative that one can tell about the current era. Consider the following story, which involves humans, computers, data, and life-or-death decisions, but where the focus is something other than intelligence-in-silicon fantasies. When my spouse was pregnant 14 years ago, we had an ultrasound. There was a geneticist in the room, and she pointed out some white spots around the heart of the fetus. “Those are markers for Down syndrome,” she noted, “and your risk has now gone up to one in 20.” She let us know that we could learn whether the fetus in fact had the genetic modification underlying Down syndrome via an amniocentesis, but amniocentesis was risky—the chance of killing the fetus during the procedure was roughly one in 300. Being a statistician, I was determined to find out where these numbers were coming from. In my research, I discovered that a statistical analysis had been done a decade previously in the UK in which these white spots, which reflect calcium buildup, were indeed established as a predictor of Down syndrome. I also noticed that the imaging machine used in our test had a few hundred more pixels per square inch than the machine used in the UK study. I returned to tell the geneticist that I believed that the white spots were likely false positives, literal white noise.

She said, “Ah, that explains why we started seeing an uptick in Down syndrome diagnoses a few years ago. That’s when the new machine arrived.”

We didn’t do the amniocentesis, and my wife delivered a healthy girl a few months later, but the episode troubled me, particularly after a back-of-the-envelope calculation convinced me that many thousands of people had gotten that diagnosis that same day worldwide, that many of them had opted for amniocentesis, and that a number of babies had died needlessly. The problem that this episode revealed wasn’t about my individual medical care; it was about a medical system that measured variables and outcomes in various places and times, conducted statistical analyses, and made use of the results in other situations. The problem had to do not just with data analysis per se, but with what database researchers call provenance—broadly, where did data arise, what inferences were drawn from the data, and how relevant are those inferences to the present situation? While a trained human might be able to work all of this out on a case-by-case basis, the issue was that of designing a planetary-scale medical system that could do this without the need for such detailed human oversight.

I’m also a computer scientist, and it occurred to me that the principles needed to build planetary-scale inference-and-decision-making systems of this kind, blending computer science with statistics, and considering human utilities, were nowhere to be found in my education. It occurred to me that the development of such principles—which will be needed not only in the medical domain but also in domains such as commerce, transportation, and education—were at least as important as those of building AI systems that can dazzle us with their game-playing or sensorimotor skills.

Whether or not we come to understand ‘intelligence’ any time soon, we do have a major challenge on our hands in bringing together computers and humans in ways that enhance human life. While some view this challenge as subservient to the creation of artificial intelligence, another more prosaic, but no less reverent, viewpoint is that it is the creation of a new branch of engineering. Much like civil engineering and chemical engineering in decades past, this new discipline aims to corral the power of a few key ideas, bringing new resources and capabilities to people, and to do so safely. Whereas civil engineering and chemical engineering built upon physics and chemistry, this new engineering discipline will build on ideas that the preceding century gave substance to, such as information, algorithm, data, uncertainty, computing, inference, and optimization. Moreover, since much of the focus of the new discipline will be on data from and about humans, its development will require perspectives from the social sciences and humanities.

While the building blocks are in place, the principles for putting these blocks together are not, and so the blocks are currently being put together in ad-hoc ways. Thus, just as humans built buildings and bridges before there was civil engineering, humans are proceeding with the building of societal-scale, inference-and-decision-making systems that involve machines, humans, and the environment. Just as early buildings and bridges sometimes fell to the ground—in unforeseen ways and with tragic consequences—many of our early societal-scale inference-and-decision-making systems are already exposing serious conceptual flaws.

Unfortunately, we are not very good at anticipating what the next emerging serious flaw will be. What we’re missing is an engineering discipline with principles of analysis and design.

The current public dialog about these issues too often uses the term AI as an intellectual wildcard, one that makes it difficult to reason about the scope and consequences of emerging technology. Let us consider more carefully what AI has been used to refer to, both recently and historically.

Most of what is labeled AI today, particularly in the public sphere, is actually machine learning (ML), a term in use for the past several decades. ML is an algorithmic field that blends ideas from statistics, computer science and many other disciplines (see below) to design algorithms that process data, make predictions, and help make decisions. In terms of impact on the real world, ML is the real thing, and not just recently. Indeed, that ML would grow into massive industrial relevance was already clear in the early 1990s, and by the turn of the century forward-looking companies such as Amazon were already using ML throughout their business, solving mission-critical, back-end problems in fraud detection and supply-chain prediction, and building innovative consumer-facing services such as recommendation systems. As datasets and computing resources grew rapidly over the ensuing two decades, it became clear that ML would soon power not only Amazon but essentially any company in which decisions could be tied to large-scale data. New business models would emerge. The phrase ‘data science’ emerged to refer to this phenomenon, reflecting both the need of ML algorithms experts to partner with database and distributed-systems experts to build scalable, robust ML systems, as well as reflecting the larger social and environmental scope of the resulting systems.This confluence of ideas and technology trends has been rebranded as ‘AI’ over the past few years. This rebranding deserves some scrutiny.

Historically, the phrase “artificial intelligence” was coined in the late 1950s to refer to the heady aspiration of realizing in software and hardware an entity possessing human-level intelligence. I will use the phrase “human-imitative AI” to refer to this aspiration, emphasizing the notion that the artificially intelligent entity should seem to be one of us, if not physically then at least mentally (whatever that might mean). This was largely an academic enterprise. While related academic fields such as operations research, statistics, pattern recognition, information theory, and control theory already existed, and often took inspiration from human or animal behavior, these fields were arguably focused on low-level signals and decisions. The ability of, say, a squirrel to perceive the three-dimensional structure of the forest it lives in, and to leap among its branches, was inspirational to these fields. AI was meant to focus on something different: the high-level or cognitive capability of humans to reason and to think. Sixty years later, however, high-level reasoning and thought remain elusive. The developments now being called AI arose mostly in the engineering fields associated with low-level pattern recognition and movement control, as well as in the field of statistics, the discipline focused on finding patterns in data and on making well-founded predictions, tests of hypotheses, and decisions.

Indeed, the famous backpropagation algorithm that David Rumelhart rediscovered in the early 1980s, and which is now considered at the core of the so-called “AI revolution,” first arose in the field of control theory in the 1950s and 1960s. One of its early applications was to optimize the thrusts of the Apollo spaceships as they headed towards the moon.

Since the 1960s, much progress has been made, but it has arguably not come about from the pursuit of human-imitative AI. Rather, as in the case of the Apollo spaceships, these ideas have often hidden behind the scenes, the handiwork of researchers focused on specific engineering challenges. Although not visible to the general public, research and systems-building in areas such as document retrieval, text classification, fraud detection, recommendation systems, personalized search, social network analysis, planning, diagnostics, and A/B testing have been a major success—these advances have powered companies such as Google, Netflix, Facebook, and Amazon.

One could simply refer to all of this as AI, and indeed that is what appears to have happened. Such labeling may come as a surprise to optimization or statistics researchers, who find themselves suddenly called AI researchers, but labels aside, the bigger problem is that the use of this single, ill-defined acronym prevents a clear understanding of the range of intellectual and commercial issues at play.

The past two decades have seen major progress—in industry and academia—in a complementary aspiration to human-imitative AI that is often referred to as “Intelligence Augmentation” (IA). Here computation and data are used to create services that augment human intelligence and creativity. A search engine can be viewed as an example of IA, as it augments human memory and factual knowledge, as can natural language translation, which augments the ability of a human to communicate. Computer-based generation of sounds and images serves as a palette and creativity enhancer for artists. While services of this kind could conceivably involve high-level reasoning and thought, currently they don’t; they mostly perform various kinds of string-matching and numerical operations that capture patterns that humans can make use of.

Hoping that the reader will tolerate one last acronym, let us conceive broadly of a discipline of “Intelligent Infrastructure” (II), whereby a web of computation, data, and physical entities exists that makes human environments more supportive, interesting, and safe. Such infrastructure is beginning to make its appearance in domains such as transportation, medicine, commerce, and finance, with implications for individual humans and societies. This emergence sometimes arises in conversations about an Internet of Things, but that effort generally refers to the mere problem of getting ‘things’ onto the Internet, not to the far grander set of challenges associated with building systems that analyze those data streams to discover facts about the world and permit ‘things’ to interact with humans at a far higher level of abstraction than mere bits.

For example, returning to my personal anecdote, we might imagine living our lives in a societal-scale medical system that sets up data flows and data-analysis flows between doctors and devices positioned in and around human bodies, thereby able to aid human intelligence in making diagnoses and providing care. The system would incorporate information from cells in the body, DNA, blood tests, environment, population genetics, and the vast scientific literature on drugs and treatments. It would not just focus on a single patient and a doctor, but on relationships among all humans, just as current medical testing allows experiments done on one set of humans (or animals) to be brought to bear in the care of other humans. It would help maintain notions of relevance, provenance, and reliability, in the way that the current banking system focuses on such challenges in the domain of finance and payment. While one can foresee many problems arising in such a system—privacy issues, liability issues, security issues, etc.—these concerns should be viewed as challenges, not show-stoppers.

We now come to a critical issue: is working on classical human-imitative AI the best or only way to focus on these larger challenges? Some of the most heralded recent success stories of ML have in fact been in areas associated with human-imitative AI—areas such as computer vision, speech recognition, game-playing, and robotics. Perhaps we should simply await further progress in domains such as these. There are two points to make here. First, although one would not know it from reading the newspapers, success in human-imitative AI has in fact been limited; we are very far from realizing human-imitative AI aspirations. The thrill (and fear) of making even limited progress on human-imitative AI gives rise to levels of over-exuberance and media attention that is not present in other areas of engineering.

Second, and more importantly, success in these domains is neither sufficient nor necessary to solve important IA and II problems. On the sufficiency side, consider self-driving cars. For such technology to be realized, a range of engineering problems will need to be solved that may have little relationship to human competencies (or human lack-of-competencies). The overall transportation system (an II system) will likely more closely resemble the current air-traffic control system than the current collection of loosely coupled, forward-facing, inattentive human drivers. It will be vastly more complex than the current air-traffic control system, specifically in its use of massive amounts of data and adaptive statistical modeling to inform fine-grained decisions. Those challenges need to be in the forefront versus a potentially distracting focus on human-imitative AI.

As for the necessity argument, some say that the human-imitative AI aspiration subsumes IA and II aspirations, because a human-imitative AI system would not only be able to solve the classical problems of AI (e.g., as embodied in the Turing test), but it would also be our best bet for solving IA and II problems. Such an argument has little historical precedent. Did civil engineering develop by envisaging the creation of an artificial carpenter or bricklayer? Should chemical engineering have been framed in terms of creating an artificial chemist? Even more polemically: if our goal was to build chemical factories, should we have first created an artificial chemist who would have then worked out how to build a chemical factory?

A related argument is that human intelligence is the only kind of intelligence we know, thus we should aim to mimic it as a first step. However, humans are in fact not very good at some kinds of reasoning—we have our lapses, biases, and limitations. Moreover, critically, we did not evolve to perform the kinds of large-scale decision-making that modern II systems must face, nor to cope with the kinds of uncertainty that arise in II contexts. One could argue that an AI system would not only imitate human intelligence, but also correct it, and would also scale to arbitrarily large problems. Of course, we are now in the realm of science fiction—such speculative arguments, while entertaining in the setting of fiction, should not be our principal strategy going forward in the face of the critical IA and II problems that are beginning to emerge. We need to solve IA and II problems on their own merits, not as a mere corollary to a human-imitative AI agenda.

It is not hard to pinpoint algorithmic and infrastructure challenges in II systems that are not central themes in human-imitative AI research. II systems require the ability to manage distributed repositories of knowledge that are rapidly changing and are likely to be globally incoherent. Such systems must cope with cloud-edge interactions in making timely, distributed decisions, and they must deal with long-tail phenomena where there is lots of data on some individuals and little data on most individuals. They must address the difficulties of sharing data across administrative and competitive boundaries. Finally, and of particular importance, II systems must bring economic ideas such as incentives and pricing into the realm of the statistical and computational infrastructures that link humans to each other and to valued goods. Such II systems can be viewed as not merely providing a service, but as creating markets. There are domains such as music, literature, and journalism that are crying out for the emergence of such markets, where data analysis links producers and consumers. And this must all be done within the context of evolving societal, ethical, and legal norms.

Of course, classical human-imitative AI problems remain of great interest as well. However, the current focus on doing AI research via the gathering of data, the deployment of deep learning infrastructure, and the demonstration of systems that mimic certain narrowly defined human skills—with little in the way of emerging explanatory principles—tends to deflect attention from major open problems in classical AI. These problems include the need to bring meaning and reasoning into systems that perform natural language processing, the need to infer and represent causality, the need to develop computationally tractable representations of uncertainty and the need to develop systems that formulate and pursue long-term goals. These are classical goals in human-imitative AI, but in the current hubbub over the AI revolution it is easy to forget that they are not yet solved.

IA will also remain quite essential, because for the foreseeable future, computers will not be able to match humans in their ability to reason abstractly about real-world situations. We will need well-thought-out interactions of humans and computers to solve our most pressing problems. And we will want computers to trigger new levels of human creativity, not replace human creativity (whatever that might mean).

It was John McCarthy (while a professor at Dartmouth, and soon to take a position at MIT) who coined the term AI, apparently to distinguish his budding research agenda from that of Norbert Wiener (then an older professor at MIT). Wiener had coined “cybernetics” to refer to his own vision of intelligent systems—a vision that was closely tied to operations research, statistics, pattern recognition, information theory, and control theory. McCarthy, on the other hand, emphasized the ties to logic. In an interesting reversal, it is Wiener’s intellectual agenda that has come to dominate in the current era, under the banner of McCarthy’s terminology. (This state of affairs is surely, however, only temporary; the pendulum swings more in AI than in most fields.)

Beyond the historical perspectives of McCarthy and Wiener, we need to realize that the current public dialog on AI—which focuses on narrow subsets of both industry and of academia—risks blinding us to the challenges and opportunities that are presented by the full scope of AI, IA, and II.

This scope is less about the realization of science-fiction dreams or superhuman nightmares, and more about the need for humans to understand and shape technology as it becomes ever more present and influential in their daily lives. Moreover, in this understanding and shaping, there is a need for a diverse set of voices from all walks of life, not merely a dialog among the technologically attuned. Focusing narrowly on human-imitative AI prevents an appropriately wide range of voices from being heard.

While industry will drive many developments, academia will also play an essential role, not only in providing some of the most innovative technical ideas, but also in bringing researchers from the computational and statistical disciplines together with researchers from other disciplines whose contributions and perspectives are sorely needed—notably the social sciences, the cognitive sciences, and the humanities.

On the other hand, while the humanities and the sciences are essential as we go forward, we should also not pretend that we are talking about something other than an engineering effort of unprecedented scale and scope; society is aiming to build new kinds of artifacts. These artifacts should be built to work as claimed. We do not want to build systems that help us with medical treatments, transportation options, and commercial opportunities only to find out after the fact that these systems don’t really work, that they make errors that take their toll in terms of human lives and happiness. In this regard, as I have emphasized, there is an engineering discipline yet to emerge for the data- and learning-focused fields. As exciting as these latter fields appear to be, they cannot yet be viewed as constituting an engineering discipline.

We should embrace the fact that we are witnessing the creation of a new branch of engineering. The term engineering has connotations—in academia and beyond—of cold, affectless machinery, and of loss of control for humans, but an engineering discipline can be what we want it to be. In the current era, we have a real opportunity to conceive of something historically new: a human-centric engineering discipline. I will resist giving this emerging discipline a name, but if the acronym AI continues to serve as placeholder nomenclature going forward, let’s be aware of the very real limitations of this placeholder. Let’s broaden our scope, tone down the hype, and recognize the serious challenges ahead.


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Amazon's Customer Journey Analytics team creates reporting and analysis for teams across the retail website. As part of the analytics team, you have the opportunity to discover insights that drive changes in how customers shop and interact with the website.We are seeking an exceptionally curious and customer focused Data Scientist responsible for identifying and solving customer pain points across the Amazon website. This candidate will conduct scientific research, build predictive models and contribute to production ML rankers to enhance the customer experience and drive long term financial value.A successful candidate will be a self-starter, comfortable with ambiguity, able to think big and be creative (while still paying careful attention to detail). They should be able to translate how data represents the customer experience, be comfortable dealing with large and complex data sets, and have experience using machine learning and econometric modeling to solve business problems. They should have strong analytical and communication skills, be able to work with product managers and software teams to define key business questions and work with the analytics team to solve them.Key responsibilities include:· Design, build and automate datasets to scale and support business needs, allowing you to focus on answering hard and ambiguous question· Identify, develop, manage, and execute research to uncover areas of opportunity and present recommendations that will shape the future of the retail website· Retrieve and analyze data using a broad set of Amazon’s data technologies and resources, knowing how, when, and which to use.· Collaborate with product, technical, business, marketing, finance, and UX leaders to gather data and metrics requirements.· Design, drive and analyze experiments to form actionable recommendations. Present recommendations to business leaders and drive decisions.· Manage and execute entire projects from start to finish including project management, data gathering and manipulation, synthesis and modeling, problem solving, and communication of insights and recommendations.· Develop and document scientific research to be shared with the greater data science community at Amazon
US, WA, Seattle
Interested in driving thought leadership on customers discovering Private Brands? We’re building intelligent data models and NLP algorithms that will transform digital marketing discovery for Private Brands at Amazon. Come join us!We are looking for a scientist to lead innovation for our discovery efforts across all placements and all page types by developing innovative algorithms to determine the right content to serve within the right context. This role has a significant global revenue impact. At the heart of our discovery engine are systems for optimizing query sourcing, merchandising allocations, experimentation infrastructure, machine learning methods for inference and metrics-driven closed loop optimizations. This role is responsible for innovation aimed at step-changing these systems, and accelerating the pace of Machine Learning and Optimization. In addition, this scientist will be required to invent new approaches in solving challenging problems like cold start product recommendation, real-time learning customer intent and personalizing contents and optimizing trade-offs between incremental Private Brands sales and the foregone advertising opportunities.To be successful in this role you will need to be comfortable defining a long-term science vision for discovery across placements, and translating that direction into specific plans for applied scientists, as well as engineering and product teams. This is a role that combines science leadership, organizational ability, technical strength, product focus and business understanding. The ideal candidate will be an independent thinker who can make convincing, information-based arguments. With a focus on bias for action, this individual will be able to work equally well with Science, Engineering, Economics and business teams. This person will have sound judgment and help recruit and groom high caliber science talent.
US, NY, New York
Amazon 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 looking for talented Applied Scientists who can help us take our products to the next level who has deep passion for building machine-learning solutions; ability to communicate data insights and scientific vision, and has a proven track record of execute complex projects.As an Applied Scientist in Machine Learning, you will:· Conduct hands-on data analysis, build large-scale machine-learning models and pipelines· Work closely with software engineers on detailed requirements, technical designs and implementation of end-to-end solutions in production· Run regular A/B experiments, gather data, perform statistical analysis, and communicate the impact to senior management· Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving· Provide technical leadership, research new machine learning approaches to drive continued scientific innovation· Be a member of the Amazon-wide Machine Learning Community, participating in internal and external MeetUps, Hackathons and Conferences· Help attract and recruit technical talent
US, WA, Seattle
Alexa is the groundbreaking, cloud-based intelligent agent that powers Fire TV with your voice. The Alexa on Fire TV team is building the science and technology behind Alexa. Come join us! Our goal is to delight our customers by adding new features to Alexa on Fire TV and by improving the accuracy of our existing speech recognition and natural language processing systems.As a Research Scientist, you will be responsible for the natural language understanding models for Fire TV use cases. Your work will directly impact our customers in the form of novel products and services that make use of speech and language technology.You will:· Ensure data quality throughout all stages of acquisition and processing, including such areas as data sourcing/collection, ground truth generation, normalization, transformation, cross-lingual alignment/mapping, etc.· Clean, analyze and select data to achieve goals· Build and release models that elevate the customer experience and track impact over time· Collaborate with colleagues from science, engineering and business backgrounds· Present proposals and results in a clear manner backed by data and coupled with actionable conclusions· Work with engineers to develop efficient data querying infrastructure for both offline and online use cases
GB, London
Machine Learning (ML) has been strategic to Amazon from the early years. We are pioneers in areas such as recommendation engines, product search, eCommerce fraud detection, and large-scale optimization of fulfillment center operations.The ML team within AWS provides opportunities to innovate in a fast-paced organization that contributes to game-changing projects and technologies that get deployed on devices and the cloud. As an ML Data Scientist in the AWS ML team, you'll partner with technology and business teams to build new services that surprise and delight our customers. You will be working with terabytes of text, images, and other types of data to solve real-world problems.You'll design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. You will help by developing new ML models, pipelines and architectures to help customers solve their critical business cases, such as autonomous driving, supply chain optimization, predictive maintenance, fraud detection and more. You will support our customers on their ML journey by helping to develop Proof of Concepts, and at the same time helping them understand the technology behind the scientific choices you make.We’re looking for top ML data scientists capable of using ML and other techniques to design, evangelize, and implement state-of-the-art solutions for never-before-solved problems.The primary responsibilities of this role are to:· Use deep learning, machine learning and analytical techniques to create scalable solutions for business problems· Design, development and evaluation of highly innovative models for predictive learning, content ranking, and anomaly detection· Interact with customers directly to understand the business problem, help and aid them in implementation of DL/ML algorithms to solve problems· Analyze and extract relevant information from large amounts of historical data to help automate and optimize key processes· Work closely with account team, research scientist teams and product engineering teams to drive model implementations and new algorithmsThis position requires travel of up to 40%.We 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.This team will be comprised of Deep Learning Architects and Data Scientists to create cutting edge solutions for clients across EMEA. We are currently recruiting for talented individuals in the following cities: London and Berlin. Discover more at https://www.amazon.jobs/en/teams/amazonai.
US, WA, Seattle
Interested in driving thought leadership on customers discovering Private Brands? We’re building intelligent data models and NLP algorithms that will transform digital marketing discovery for Private Brands at Amazon. Come join us!We are looking for a senior scientist to lead innovation for our discovery efforts across all placements and all page types by developing innovative algorithms to determine the right content to serve within the right context. This role has a significant global revenue impact. At the heart of our discovery engine are systems for optimizing query sourcing, merchandising allocations, experimentation infrastructure, machine learning methods for inference and metrics-driven closed loop optimizations. This Senior Applied Scientist is responsible for innovation aimed at step-changing these systems, and accelerating the pace of Machine Learning and Optimization. In addition, this scientist will be required to invent new approaches in solving challenging problems like cold start product recommendation, real-time learning customer intent and personalizing contents and optimizing trade-offs between incremental Private Brands sales and the foregone advertising opportunities.To be successful in this role you will need to be comfortable defining a long-term science vision for discovery across placements, and translating that direction into specific plans for applied scientists, as well as engineering and product teams. This is a role that combines science leadership, organizational ability, technical strength, product focus and business understanding. The ideal candidate will be an independent thinker who can make convincing, information-based arguments. With a focus on bias for action, this individual will be able to work equally well with Science, Engineering, Economics and business teams. This person will have sound judgment and help recruit and groom high caliber science talent.
US, WA, Seattle
Do you have a passion for diving deep to uncover key insights that drive critical business decisions? If yes, the Customer Behavior Cross Channel Optimization (CBA-XO) team is looking for somebody with your enthusiasm and skills to work as part of the WW central team.Scientists and Economists at Amazon are solving some of the most challenging applied science questions in the tech sector. Amazon scientists apply the frontier of ML algorithms to market design, pricing, forecasting, program evaluation, online advertising, and other areas. Our scientists and economists build statistical models using our world class data systems, and apply economic theory to solve business problems in a fast moving environment. A career at Amazon affords scientists the opportunity to work with data of unparalleled quality, apply rigorous applied science approaches, and work with some of the most talented applied econometricians and data scientists in the trade.The Customer Behavior Analytics (CBA) organization owns Amazon’s insights pipeline, from data collection to deep analytics. We aspire to be the place where Amazon teams come for answers, a trusted source for data and insights that empower our systems and business leaders to make better decisions. Our outputs shape Amazon product and marketing teams’ decisions and thus how Amazon customers see, use, and value their experience. CBA-XO’s mission is to make Amazon’s marketing the most measurably effective in the world. Our long-term objective is to measure the incremental impact of all Amazon’s marketing investments on consumer perceptions, actions, and sales. This requires measuring Amazon’s marketing comparably and consistently across channels, business teams and countries using a comprehensive approach that integrates all Paid, Owned and Earned marketing activity. As the experts on marketing performance we will lead the Amazon worldwide marketing community by providing critical cross-country insights that can power marketing best practices and tenets globally.We are looking for a seasoned leader to manage a talented team of machine learning scientists and economists to build long-term causal estimation products using a combination of econometrics, machine learning and statistics leveraging the power of big data. These products lay the foundation of several key initiatives and strategic program investments at Amazon, generating multiple $Bns in incremental value across the company.Key Responsibilities:- Applies deep expertise in causal modeling to develop large-scale systems that are deployed across the company. Reviews and audits modeling processes and results for other scientists, both junior and senior.- Describes strategic importance of vision inside and outside of team. Identifies business opportunities, define the problem and roadmap to solve it. Brings a department or company-wide perspective in decision making.- Partners with the Product and Engineering teams to build production level systems to estimate the incremental impact of Amazon’s Marketing- Leads the project plan from a scientific perspective on small to medium product launches including identifying potential risks, key milestones, and paths to mitigate risks- Coaches and gives feedback to direct reports to help develop talent and support career development- Sets and balances goals across team to optimize performance against department goals and employee development. Identifies resource needs for the team.
US, WA, Seattle
The Sustainability Science and Innovations (SSI) team is looking for an Applied Scientist to join our team in building customer-focused sustainability products. The SSI team applies Machine Learning, Data Science, and Econometrics/Economics to derive actionable insights about the complex economy of Amazon’s retail business. We also develop Statistical Models and Algorithms to drive strategic business decisions and improve operations. We are an interdisciplinary team of Economists, Engineers, and Scientists incubating and building day one sustainability solutions using cutting-edge technology, to solve some of the toughest business problems at Amazon.This is a unique, high visibility opportunity for someone who wants to have business impact, dive deep into large-scale economic problems, enable measurable actions on the Sustainable economy, and work closely with scientists and economists. We are particularly interested in candidates with experience building predictive models and working with distributed systems.
GB, London
Machine Learning (ML) has been strategic to Amazon from the early years. We are pioneers in areas such as recommendation engines, product search, eCommerce fraud detection, and large-scale optimization of fulfillment center operations.The ML team within AWS provides opportunities to innovate in a fast-paced organization that contributes to game-changing projects and technologies that get deployed on devices and the cloud. As a Sr. ML Data Scientist in the AWS ML Solutions Lab team, you'll partner with technology and business teams to build new services that surprise and delight our customers. You will be working with terabytes of text, images, and other types of data to solve real-world problems. You'll design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience.You'll design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. You will help by developing new ML models, pipelines and architectures to help customers solve their critical business cases, such as autonomous driving, supply chain optimization, predictive maintenance, fraud detection and more. You will support our customers on their ML journey by helping to develop Proof of Concepts, and at the same time helping them understand the technology behind the scientific choices you make.We’re looking for Senior ML Data Scientists capable of using ML and other techniques to design, evangelize, and implement state-of-the-art solutions for never-before-solved problems.The primary responsibilities of this role are to:· Use deep learning, machine learning and analytical techniques to create scalable solutions for business problems· Design, development and evaluation of highly innovative models for predictive learning, content ranking, and anomaly detection· Interact with customers directly to understand the business problem, help and aid them in implementation of DL/ML algorithms to solve problems· Analyze and extract relevant information from large amounts of historical data to help automate and optimize key processes· Work closely with account team, research scientist teams and product engineering teams to drive model implementations and new algorithmsThis position requires travel of up to 40%.We 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.This team will be comprised of Deep Learning Architects and Data Scientists to create cutting edge solutions for clients across EMEA. We are currently recruiting for talented individuals in the following cities: London and Berlin. Discover more at https://www.amazon.jobs/en/teams/amazonai.
US, NY, New York
How can Amazon improve the advertising experience for customers around the world? How can we help advertisers and customers find each other in a meaningful way? Amazon Advertising creates and transforms the connection between retailers, service providers and customers. Join the Analytics & Insights Team to contribute to product and service solutions that allow us to solve this with data science. If you are passionate about developing analytics and testing solutions to solve business problems, and are looking for a team that drives results to help influence Amazon business decisions, this is the right place for you.The Analytics & Insights Professional Services Team is looking for a Head of Advertising Experimentation Team to lead a team of Data Scientists and Business Analysts who analyze big data to build models and algorithms that power our advertising experimentation services and products. We work with advertisers on recommendations for testing in order to improve their advertising effectiveness and strive to better understand the advertising and non-advertising features that best influence and predict advertising campaign performance and incrementality. In this role, you will set the vision and direction for the team and collaborate with internal stakeholders across product and services to scale and advance our experimentation and incrementality testing offerings. The ideal candidate must be willing to effectively lead the team, project-manage and prioritize across multiple stakeholders and tasks, exhibit strong problem-solving skills and be ready to jump into a fast-paced, dynamic and fun environment.Responsibilities:· Lead and provide coaching to the Advertising Experimentation Team including Data Scientists and Business Analysts.· Partner with advertisers and experimentation teams to generate A/B and incrementality test recommendations to improve marketing effectiveness and inform their future marketing investments.· Work with product, data science, experimentation and analytics teams to share knowledge from performance tests, design packaged experimentation insights and inform future product roadmaps.· Use Amazon’s unique data, analyze huge and complex data sets, design and implement solutions using a range of data science methodologies to solve complex business problems.· Demonstrate deep analytical ability, and develop great expertise in Amazon’s proprietary metrics, working to constantly evolve how we analyze and communicate data driven insights to our advertisers.· Build consensus with business stakeholders on how your models and algorithms will drive the optimal results for Amazon customers.· Educate advertisers and internal teams on performance and incrementality testing by writing whitepapers and knowledge documentation and delivering learning sessions.