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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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US, WA, Seattle
About the Analytics Team :The Analytics team is a small, central group in the AWS Support & Managed Services organisation. The team comprises members from a range of skill-sets such as Strategy & Planning, Data Science and Business Intelligence. The Analytics team is responsible for pushing our Product, Process and Customer understanding and insight beyond the here and now to create value for the long-term.About the Role:If you love driving critical thinking and insight into challenging, unstructured problems and working with leaders in the organization to bring those insights into action, this role is for you. This is a hands-on role, however, being a part of a small team gives opportunity to take ownership of the customer / business problem across the spectrum.Data Science Analytics topics include, but not limited to:1. Product / Value Proposition design2. Customer Experience improvement3. Business Planning & Forecasting4. Predictive Analytics (a number of customer-centric use-cases)5. NLP / Text mining to identify themes in customer feedback
US, NY, New York
The Alexa Science and Machine Learning team’s goal is to make voice interfaces ubiquitous and as natural as speaking to a human. Deep learning at this massive scale requires new research and development. The team is responsible for cutting-edge research and development in virtually all fields of Human Language Technology: Automatic Speech Recognition (ASR), Artificial Intelligence (AI), Natural Language Understanding (NLU), Question Answering, Dialog Management, and Text-to-Speech (TTS).As part of our speech and language team, you will work alongside internationally recognized experts to develop novel algorithms and modeling techniques to advance the state-of-the-art in spoken language understanding. Your work will directly impact millions of our customers in the form of products and services that make use of speech and language technology. You will gain hands on experience with Amazon’s heterogeneous speech, text, and structured data sources, and large-scale computing resources to accelerate advances in spoken language understanding. It is not imperative to have experience in ASR. We have scientists building production models released to Echo customers, who had no prior speech experience, but very strong in ML, statistics, coding (and “can do” spirit!).We are hiring in all areas of spoken language understanding: ASR, NLU, text-to-speech (TTS), and Dialog Management.
US, WA, Seattle
About Prime Video: Prime Video is changing the way people watch movies and TV shows, offering more than 150,000 new release movies, next-day television shows and classic favorites available to rent or purchase on-demand, and more than 38,000 titles available to customers with an Amazon Prime membership. We believe so deeply in the mission of our video offering that we've launched our own Amazon Studios to create Original and Exclusive content. With an Amazon Prime membership, customers can have unlimited access to thousands of titles for no additional charge, including popular and award-winning Prime Originals like Jack Ryan, Fleabag and The Marvelous Mrs. Maisel.About the team: The vision for our team is to inspire our customers to engage with all that Prime Video brand has to offer. To achieve our vision, we create product and technology solutions that drive incremental activation and engagement of PV customers worldwide. We obsess over finding effective ways to reach active and inactive customers with relevant and timely content that drives traffic to the PV experience. Using smart rules and machine learning we generate relevant, timely, and personalized engagement opportunities via a broad portfolio of both in-app and out-of-app experiences, on a fully automated basis.About the role: We seek an experienced Senior Applied Scientist to join us in defining and designing a multi-channel campaign management system to increase customer engagement, activation and global adaption of prime video. This effort is guaranteed to push the boundaries of intelligent marketing automation and you will be in the center of defining the science models of these automated engagement experiences. You should expect to exercise both your coding skills and creative research thinking as you map real world processes to ML enabled systems. You will spend your time as a hands-on machine learning practitioner and a research leader. You will play a key role on the team, building and guiding machine learning models from the ground up. If you’re looking for an opportunity to make a big impact in a global business with a startup culture, we’re looking for you.
US, CA, Palo Alto
Amazon is building a world class advertising business and defining and delivering a collection of self-service performance advertising products that drive discovery and sales of merchandise. 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 systems and algorithms operate on one of the world's largest product catalogs, matching shoppers with products - with a high relevance bar and strict latency constraints. 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 talented, entrepreneurial, and self-driven leader to take charge of allocating and pricing Sponsored Product ads in product pages through real-time auction. You'll be pioneering within a multi-dimensional optimization space which requires us to balance the interests of advertisers, shoppers, and Amazon. You’ll drive the engineering and science strategies within Sponsored Products organization, while also owning a broad mandate to influence various teams spread across Amazon retail business. You will be responsible for hiring and developing the individuals within your team and fostering a transparent, collaborative development environment.Responsibilities:· Own technical vision and direction for our product pages ad auctions and accompanying ML-based optimization systems.· Dive deep into performance and operational data and work closely with product, business and engineering teams to develop a compelling vision and roadmap.· Present technical plans to senior executives and key stakeholders and evangelize that technical vision within Amazon.· Hire, develop and retain top tier technical talent.· Drive efficient execution of new initiatives, including collaboration with advertising and partner teams.· Manage engineering and science teams responsible for critical high-availability infrastructure delivering ads across Amazon's sites and devices worldwide.
US, WA, Seattle
Where will Amazon's growth come from in the next year? What about over the next five? Which product lines are poised to quintuple in size? Are we investing enough in our infrastructure, or too much? How do our customers react to changes in prices, product selection, or delivery times? These are among the most important questions at Amazon today. The Forecasting team in the Supply Chain Optimization Technologies (SCOT) organization is dedicated to answering these questions using statistical methods. We develop cutting edge data pipelines, build accurate predictive models, and deploy automated software solutions to provide forecasting insights to business leaders at the most senior levels throughout the company. We are looking for a talented, driven, and analytical researcher to help us answer these (and many more) questions.We are building a new team to develop predictive models and provide business insights on seller behavior on the Amazon Consumer platform. We will build models to produce forecasts of unit sales and revenue by seller segments and drive adoption of these forecasts by various teams within Amazon for financial and operations planning. We will provide insights on the impact of seller selection, product selection and fees on the long-term growth of the business, and provide recommendations to drive future growth of the seller platform.This Data Scientist role will design quantitative systems and forecasting models that generate multi-billion dollar predictions of the highest level of visibility and importance for Amazon's financial and operational planning. A successful candidate will be a problem solver who enjoys diving into data, is excited by difficult modeling challenges, and possesses strong communication skills to effectively interface between technical and business teams. As a Data Scientist on this team, you will collaborate directly with economists and statisticians to produce modeling solutions, you will partner with software developers and data engineers to build end-to-end data pipelines and production code, and you will have exposure to senior leadership as we communicate results and provide scientific guidance to the business.Key Responsibilities:· Implement statistical methods to solve specific business problems utilizing code (Python, R, Scala, etc.).· Improve upon existing methodologies by developing new data sources, testing model enhancements, and fine-tuning model parameters.· Directly contribute to the design and development of automated forecasting systems.· Build customer-facing reporting tools to provide insights and metrics which track forecast performance and explain variance.· Collaborate with researchers, software developers, and business leaders to define product requirements, provide analytical support, and communicate feedback.· Presenting critical data in a format that is immediately useful to answer questions about the inputs and outputs of Forecasting systems and improving their performance.Amazon is an Equal Opportunity Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation.
US, NY, New York
Business/Team IntroductionThe Amazon Demand Forecasting team seeks a Data Scientist with strong analytical and communication skills to join our team. We develop sophisticated algorithms that involve learning from large amounts of data, such as prices, promotions, similar products, and a product’s attributes, in order to forecast the demand of over 190 million products world-wide. These forecasts are used to automatically order more than $200 million worth of inventory weekly, establish labor plans for tens of thousands of employees, and predict the company’s financial performance. The work is complex and important to Amazon. With better forecasts we drive down supply chain costs, enabling the offer of lower prices and better in-stock selection for our customers.Data Scientist ResponsibilitiesIn a typical day, you will work closely with talented machine learning scientists, statisticians, software engineers, and business groups. Your work will include cutting edge technologies that enable implementation of sophisticated models on big data. As a successful data scientist in our Demand Forecasting team, you are an analytical problem solver who enjoys diving into data, is excited about investigations and algorithms, can multi-task, and can credibly interface between technical teams and business stakeholders. Your analytical abilities, business understanding, and technical savvy will be used to identify specific and actionable opportunities to solve existing business problems in Demand Forecasting, through collaboration with engineering, research, and business teams. Your expertise in synthesizing and communicating insights and recommendations to audiences of varying levels of technical sophistication will enable you to answer specific business questions and innovate for the future.Major responsibilities include:· Translating Demand Forecasting business questions and concerns into specific analytical questions that can be answered with available data using statistical and machine learning methods; working with engineers to produce the required data when it is not available· Providing feedback to our science and engineering teams on the applicability of technical solutions from the business perspective· Presenting critical data in a format that is immediately useful to answer questions about the inputs and outputs of Demand Forecasting systems and improving their performance· Communicating verbally and in writing to business customers with various levels of technical knowledge, educating them about our systems, as well as sharing insights and recommendations· Improving upon existing Demand Forecasting statistical or machine learning methodologies by developing new data sources, testing model enhancements, running computational experiments, and fine-tuning model parameters for new forecasting models· Supporting decision making by providing requirements to develop analytic capabilities, platforms, pipelines and metrics then using them to analyze trends and find root causes of forecast inaccuracy· Formalizing assumptions about how demand forecasts are expected to behave, creating definitions of outliers, developing methods to systematically identify these outliers, and explaining why they are reasonable or identifying fixes for them· Utilizing code (Python, R, Scala, SQL etc.) for analyzing data and building statistical and machine learning models and algorithmsAmazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation
CA, ON, Toronto
We are pleased to announce an exclusive invitation only hiring event in Tel-Aviv, Israel in February 2020 to find talented Sr. Software Development Engineers to join the Amazon Advertising teams in Toronto!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.Our systems and algorithms operate on one of the world's largest product catalogs, matching shoppers with advertised products with a high relevance bar and strict latency constraints. We work hand-in-hand with Machine Learning and NLP research scientists to come up with novel solutions that retrieves highly relevant ads. We consistently strive to improve the customer search and detail page experiences. You will drive appropriate technology choices for the business, lead the way for continuous innovation, and shape the future of e-commerce. This is an opportunity to make a significant impact on the future of the Amazon vision.If you have the passion to build heuristics as well as machine learned models based on large-scale datasets, then the Amazon Sponsored Products Relevance team is a great place to be. The ideal candidate would have worked on distributed systems such as building data-driven systems or infrastructure that handle requests at large-scale or both. Experience with Machine Learning will be a bonus. Overtime, we do expect you to learn about Machine Learning and how feature engineering is done within Amazon. The opportunity challenges you to bring both science and engineering together to identify the most relevant Ads on Amazon’s product detail pages. Your work will impact the user experience of millions of Amazon’s customers.As a Data Scientist in our team, you will collaborate directly with developers and scientist to produce modeling solutions, you will partner with software developers and data engineers to build end-to-end data pipelines and production code, and you will have exposure to senior leadership as we communicate results and provide scientific guidance to the business. You will analyze large amounts of business data, automate and scale the analysis, and develop metrics that will enable us to continually delight our customers worldwide. As a successful data scientist, you are an analytical problem solver who enjoys diving into data, is excited about investigations and algorithms, can multi-task, and can credibly interface between technical teams and business stakeholders. Your analytical abilities, business understanding, and technical savvy will be used to identify specific and actionable opportunities to solve existing business problems and look around corners for future opportunities. Your expertise in synthesizing and communicating insights and recommendations to audiences of varying levels of technical sophistication will enable you to answer specific business questions and innovate for the future.Major responsibilities include:· Utilizing code (Python, R, Scala, etc.) for analyzing data and building statistical models to solve specific business problems.· Improve upon existing methodologies by developing new data sources, testing model enhancements, and fine-tuning model parameters.· Directly contribute to the design and development of automated selection systems· Build customer-facing reporting tools to provide insights and metrics which track system performance· Collaborate with researchers, software developers, and business leaders to define product requirements and provide analytical support· Communicating verbally and in writing to business customers and leadership team with various levels of technical knowledge, educating them about our systems, as well as sharing insights and recommendations
IN, KA, Bangalore
Interested in Amazon Alexa? We’re building the speech and language solutions behind Amazon Alexa and Amazon products and services such as the Amazon Echo and Dot. Come join us!We're looking for a Manager, Research Science who combines exceptional technical, research and analytical capabilities to build and lead a team that will be integral to the continued improvement of Amazon Alexa. As a Research Manager, you will be responsible for leading a team of researchers and data experts in the design, development, testing, and deployment of speech and language data processes and model improvements, supporting a range of products.This involves:· Conducting and coordinating process development leading to improved and streamlined processes for model development. Strong customer focus is essential.· Providing technical and scientific guidance to your team members.· Communicating effectively with senior management as well as with colleagues from science, engineering and business backgrounds.· Supporting the career development of your team members.A successful candidate will have an established background in developing customer-facing experiences, a strong technical ability, demonstrated experience in people management, excellent project management skills, great communication skills, and the motivation to achieve results in a fast-paced environment.
US, WA, Seattle
Amazon.com seeks an exceptional Data Scientist to join our Sponsored Products Marketplace Finance team. The role will be a key partner to Amazon’s Sponsored Products Marketplace business, a high-growth business with a range of products offerings for advertisers and using “pay-per-click” business model. We love data and we have lots of it. We’re looking for a top analytical mind capable of understanding our complex ecosystem of advertisers participating in a pay-per-click model as well as the relationship this business has with Amazon’s core eCommerce business – and leveraging this knowledge to help turn the flywheel of the business.As a key analytics partner, you will have the opportunity to work on one of the world's largest consumer and advertising data sets, as well as influence the long-term evolution of our products and the way they fit in Amazon's ecosystem to develop insights into different aspects of our business (monetization, shopper experience, advertiser experience, marketplace dynamics).This role requires an individual with excellent business, communication, and technical skills, enabling collaboration with various functions, including economists and other finance leaders, product managers, software engineers, data scientists as well as senior leadership.The successful candidate will be a self-starter comfortable with ambiguity, with strong attention to detail, an ability to work in a fast-paced, high-energy and ever-changing environment. The drive and capability to shape the business group strategy is a must.This role will influence the direction of the business by leveraging our data to deliver insights that drive decisions and actions. The role will involve translating broad business problems into specific analytics projects, conducting deep quantitative analyses, and communicating results effectively. We see a high potential for influence and growth in this role as we transform our data into actionable insights to continue to fuel the growth of this business. The role will help the organization identify, evaluate, and evangelize new techniques and tools to continue to improve our ability to deliver value to Amazon’s customers.
US, WA, Seattle
The Amazon Devices team designs and engineers high-profile consumer electronics, including the best-selling Kindle family of products. We have also produced groundbreaking devices like Fire tablets, Fire TV, Amazon Dash, and Amazon Echo.What will you help us create?The Team: How often have you had an opportunity to be a founding member of a team that is solving a significant problem through innovative technology? Would you like to know more about how we are envisioning the use of machine learning, AI and linear programming to solve these problems? If this sounds intriguing, then we’d like to talk to you about a role on a new Amazon team that's tackling a set of problems requiring significant innovation and scaling.As a data scientist, you will design, evangelize, and implement state-of-the-art solutions for never-before-solved problems, helping Amazon Device to provide customer great products. This role will be a key member of a Science and Data technology team based in Seattle, WA. You will work closely with other research scientists, machine learning experts, engineers to design and run experiments, research new algorithms, and find new ways to improve Amazon Device Services & Software products. You will partner with technology and product leaders to solve business and technology problems using scientific approaches to build new services that surprise and delight our customers. Our scientists work closely with software engineers to put algorithms into practice. They also work on cross-disciplinary efforts with other scientists within Amazon.The key responsibility for this role include:· Define proper output business Metrics, and build input models to identify patterns and drivers of the output. Drive actions at scale using scientifically-based methods and decision making.· Design and develop complex mathematical, statistical, simulation and optimization models and apply them to define strategic and tactical needs and drive the appropriate business and technical solutions· Design experiments, test hypotheses, and build actionable models· Prototype these models by using modeling languages such as R or in software languages such as Python.· Work with software engineering teams to drive scalable, real-time implementations· Utilizing Amazon systems and tools to effectively work with terabytes of data
US, MA, Cambridge
Amazon is looking for a passionate, talented, and inventive Scientist with a strong machine learning background to help build industry-leading Speech and Language technology. Our mission is to push the envelope in Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), and Audio Signal Processing, in order to provide the best-possible experience for our customers.As a Scientist, you will work with talented peers to develop novel algorithms and modeling techniques to advance the state of the art in spoken language understanding. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in spoken language understanding.We are hiring in all areas of spoken language understanding: ASR, NLU, text-to-speech (TTS), and Dialog Management.