"This technology will be transformative in ways we can barely comprehend"

A judge and some of the finalists from the Alexa Prize Grand Challenge 3 talk about the competition, the role of COVID-19, and the future of socialbots.

Human beings are social creatures, and conversations are what connect us—they enable us to share everything from the prosaic to the profound with the people that matter to us. Living through an era marked by pandemic-induced isolation means many of those conversations have shifted online, but the connection they provide remains essential.

So what happens when you replace one of the human participants in a conversation with a socialbot? What does it mean to have an engaging conversation with an AI assistant? How can that kind of conversation prove to be valuable, and can it provide its own kind of connection?

Application period for next Alexa Prize challenge opens

The Amazon Alexa Prize team encourages all interested teams to apply for the Grand Challenge 4 by 11:59 p.m. PST on October 6, 2020.

The participants in this year’s Alexa Prize contest are driven by those questions. Amazon recently announced that a team from Emory University has won the 2020 Alexa Prize. We talked to that team, along with a judge from this year’s competition, as well as representatives from the other finalist teams at Czech Technical University, Stanford University, University of California, Davis, and University of California, Santa Cruz. We wanted to learn what drives them to participate, how COVID-19 has influenced their work and what they see as the possibilities and challenges for socialbots moving forward.

Alexa Prize Grand Challenge 3 winners share their work | Amazon Science

Q: What inspired you to participate in this year’s competition?

Sarah Fillwock, team leader, Emora, Emory University: We had a group of students who were interested in dialogue system research, some of whom had actually participated in the Alexa Prize in its previous years, and we all knew that the Alexa Prize offers a really unique opportunity for anyone interested in this type of work. It is really exciting to use the Alexa device platform to launch a socialbot, because we are able to get hundreds of conversations a day between our socialbot and human users, which really allows for quick turnaround time when assessing whether or not our hypotheses and strategies are improving the performance of our dialogue system.

Marilyn Walker, faculty advisor, Athena, University of California, Santa Cruz: In our Natural Language and Dialogue Systems lab, our main research focus is dialogue management and language generation. Conversational AI is a very challenging problem, and we felt like we could have a research impact in this area. The field has been developing extremely quickly recently, and the Alexa Prize offers an opportunity to try out cutting-edge technologies in dialogue management and language generation on a large Alexa user population.

Amazon Alexa Prize Finalists 2020
The five Alexa Prize finalist teams: Czech Technical University in Prague; Emory University; Stanford University; the University of California, Davis; and the University of California, Santa Cruz.

Vrindavan (Davan) Harrison, team leader, Athena, UCSC: As academics, our primary focus is on research. This year’s competition aimed at being more research-oriented, allowing the teams to spend more time on developing new ideas.

Kai-Hui Liang, team lead, Gunrock, University of California, Davis: Our experience in last year’s competition motivated us to join again as we realized there is still a large room for improvement. I’m especially interested in how to find topics that engage users the most, including trying different ways to elicit and reason about users’ interests. How can we retrieve content that is relevant and interesting, and make the dialog flow more naturally?

Jan Pichl, team leader, Alquist, Czech Technical University: Since the first year of the Alexa Prize competition, we have been developing Alquist to deliver a wide range of topics with a closer focus on the most popular ones. The first Alquist guided a user through the conversation quite strictly. We learned quickly that we needed to introduce more flexibility and let the user be "in charge". With that in mind, we have been pushing Alquist in that direction. Moreover, we want Alquist to manage dialogue utilizing the knowledge graph, and suggest relevant information based on the previously discussed topics and entities.

Christopher D. Manning, faculty advisor, Chirpy Cardinal, Stanford University: It was our first time doing the Alexa Prize, and the team really hadn’t done advance preparation, so it’s all been a wild ride—by which I mean a lot of work and stress for everyone on the team. But it was super exciting that we were largely able to catch up with other leading teams who have been doing the competition for several years.

Hugh Howey, judge and science fiction author: Artificial intelligence is a passionate interest of mine. As a science fiction author, I have the freedom to write about most anything, but the one topic I keep coming back to is the impact that thinking machines already have on our lives and how that impact will only expand in the future. So any chance to be involved with those doing work and research in the field is a no-brainer for me. I leapt at the chance like a Boston Dynamics dog.

Q: What excites you about the potential of socialbots?

Hugh Howey (Judge): This technology will be transformative in ways we can barely comprehend. Right now, the human/computer interface is a bottleneck. It takes a long time for us to tell our computers what we want them to do, and they'll generally only do that thing the one time and forget what it learned. In the future, more and more of the trivial will be automated. This will free up human capital to tackle larger problems. It will also bring us together by removing language barriers, by helping those with disabilities, and eventually this technology will be available to anyone who needs it.

Jinho D. Choi, faculty advisor, Emory: It has been reported that more than 44 million adults in US have mental health issues such as anxiety or depression. We believe that developing an innovative socialbot that comforts people can really help those with mental health conditions, who are generally afraid of talking to other human beings. You may wonder how artificial intelligence can convey a human emotion such as caring. However, humans have used their own creations, such as arts and music, to comfort themselves. It is our vision to advance AI, the greatest invention of humankind, to help individuals learn more about their inner selves so they can feel more positive about themselves, and have a bigger impact in the world.

Ashwin Paranjape, co-team leader, Stanford: As socialbots become more sophisticated and prevalent, increasing numbers of people are chatting with them regularly. As the name suggests, socialbots have the potential to fulfill social needs, such as chit-chatting about everyday life, or providing support to a person struggling with mental health difficulties. Furthermore, socialbots could become a primary user interface through which we engage with the world—for example, chatting about the news, or discussing a book.

Sarah Fillwock, Emory: Our experience in this competition has really solidified this idea of the potential of socialbots being value to people who need support and are in troubling situations. I think that the most compelling role for socialbots in global challenges is to provide a supportive environment to allow people to express themselves, and explore their feelings with regard to whatever dramatic event is going on. This is especially important for vulnerable populations, such as those who do not have a strong social circle or have reduced social contact with others, prohibiting them from being able to achieve the feeling of being valued and understood.

Q: What are the main challenges to realizing that potential?

Abigail See, co-team leader, Stanford: Currently, socialbots struggle to make sense of long, involved conversations, and this limits their ability to talk about any topic in depth. To do this better, socialbots will need to understand what a particular user wants—not only in terms of discussion topics, but also what kind of conversation they want to have. Another important challenge is to allow users to take more initiative, and drive the conversation themselves. Currently, socialbots tend to take more initiative, to ensure the conversation stays within their capabilities. If we can make our socialbots more flexible, they will be much more useful and engaging to people.

Sarah Fillwock, Emory: One major challenge facing the field of dialogue system research is establishing a best practice for evaluation of the performance of dialogue approaches. There is currently a diverse set of evaluation strategies that the research community uses to determine how well their new dialogue approach performs. Another challenge is that dialogues are more than just a pattern-matching problem. Having a back-and-forth dialogue on any topic with another agent tends to involve planning towards achieving specific goals during the conversation as new information about your speaking partner is revealed. Dialogues also rely a lot on having a foundation of general world knowledge that you use to fully understand the implications of what the other person is saying.

Amazon releases Topical Chat dataset

The text-based collection of more than 235,000 utterances will help support high-quality, repeatable research in the field of dialogue systems.

Marilyn Walker, UCSC: There’s a shortage of large annotated conversational corpora for the task of open-domain conversation. For example, progress in NLU has been supported by large annotated corpora, such as Penn Treebank, however, there are currently no such publicly available corpora for open-domain conversation. Also, a rich model of individual users would enable much more natural conversations, but privacy issues currently make it difficult to build such models.

Hugh Howey (Judge): The challenge will be for our ethics and morality to keep up with our gizmos. We will be far more powerful in the future. I only hope we'll be more responsible as well.

Q: What role has the COVID-19 pandemic played in your work?

Jurik Juraska, team member, UCSC: The most immediate effect the onset of the pandemic had on our socialbot was, of course, that it could not just ignore this new dynamic situation. Our socialbot had to acknowledge this new development, as that was what most people were talking about at that point. We would thus have Athena bring up the topic at the beginning of the conversation, sympathizing with the users' current situation, but avoiding wallowing in the negative aspects of it. In the feedback that some users left, there were a number of expressions of gratitude for the ability to have a fun interaction with a socialbot at a time when direct social interaction with friends and family was greatly restricted.

Kai-Hui Liang, UC Davis: We noticed an evident difference in the way Alexa users reacted to popular topics. For example, before COVID-19, many users gave engaging responses when discussing their favorite sports to watch, their travel experiences, or events they plan to do over the weekend. After the breakout of COVID-19, more users replied saying there’s no sports game to watch or they are not able to travel. Therefore, we adapted our topics to better fit the situation. We added discussion about their life experience during the quarantine (eg. how their diet has changed or if they walk outside daily to stay healthy). We also observed more users having negative feelings potentially due to the quarantine. For instance, some users said they feel lonely and they miss their friends or family. Therefore, we enhanced our comforting module that expresses empathy through active listening.

Abigail See, Stanford: As the pandemic unfolded, we saw in real time how users changed their expectations of our socialbot. Not only did they want our bot to deliver up-to-date information, they also wanted it to show emotional understanding for the situation they were in.

Sarah Fillwock, Emory: When COVID became a significant societal issue, we tried two things: we had an experience-oriented COVID topic where our bot discussed with people how they felt about COVID in a sympathetic and reassuring atmosphere, and we had a fact-oriented COVID topic that gave objective information. What we observed was that people had a much stronger positive reaction to the experience-oriented COVID-19 approach than the fact-oriented COVID-19 approach, and seemed to prefer it when talking. This really gave us some empirical evidence that social agents have a strong potential to be helpful in times of turmoil by giving people a safe and caring space to talk about these major events in their life since people responded positively to our approach at doing this.

Q: Lastly, are there any particular advancements in the fields of NLU, dialogue management, conversational AI, etc., that you find promising?

Jan Pichl, Czech Technical University: It is exciting to see the capabilities of the Transformer-based models these days. They are able to generate large articles or even whole stories that are coherent. However, they demand a lot of computation power during the training phase and even during the runtime. Additionally, it is still challenging to use them in a socialbot when you need to work with constantly changing information about the world.

Abigail See, Stanford: As NLP researchers, we are amazed by the incredible pace of progress in the field. Since the last Alexa Prize in 2018, there have been game-changing advancements, particularly in the use of large pretrained language models to understand and generate language. The Alexa Prize offers a unique opportunity for us to apply these techniques, which so far have mostly been tested only on neat, well-defined tasks, and put them in front of real people, with all the messiness that entails! In particular, we were excited to explore the possibility of using neural generative models to chat with people. As recently as the 2018 Alexa Prize, these models generally performed poorly, and so were not used by any of the finalist teams. However, this year, these systems became an important backbone of our system.

Sarah Fillwock, Emory: The work people have been putting into incorporating common sense knowledge and common sense reasoning into dialogue systems is one of the most interesting directions of the current conversational AI field. A lot of the common sense knowledge we use is not explicitly detailed in any type of data set as people have learned them through physical experience or inference over time, so there isn’t necessarily any convenient way to currently accomplish this goal. There have been a lot of attempts to see how far a language modeling approach to dialogue agents can go, but even using huge dialogue data sets and highly complex models still results in hit-and-miss success at common sense information. I am really looking forward to the dialogue approaches and dialogue resources that more explicitly try to model this type of common sense knowledge.

Amazon Science Newsletter Project Kuiper.jpg
Get more from Amazon Science
Sign up for our monthly newsletter

Work with us

See More Jobs
US, WA, Seattle
Why this job is awesome?· This is SUPER high-visibility work: Our mission is to provide consistent, accurate, and relevant delivery, customer service, and product safety information to every single page on every Amazon-owned site.· MILLIONS of customers will be impacted by your contributions: The changes we make directly impact the customer experience on every Amazon site. This is a great position for someone who likes to leverage Machine learning technologies to solve the real customer problems, and also wants to see and measure their direct impact on customers.· We are a cross-functional team that owns the ENTIRE delivery experience for customers: From the business requirements to the technical systems that allow us to directly affect the on-site experience from a central service, business and technical team members are integrated so everyone is involved through the entire development process.- Do you want to join an innovative team of scientists and engineers who use machine learning and statistical techniques to deliver the best delivery experience, product safety, and customer service on every Amazon-owned site?- Are you excited by the prospect of analyzing and modeling terabytes of data on the cloud and create state-of-art algorithms to solve real world problems?- Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company?- Do you like to innovate and simplify?If yes, then you may be a great fit to join one of the Operations Technology Machine Learning teams.Major responsibilities:· Research and implement machine learning and statistical techniques to create scalable and effective models in Delivery, Customer Service, and Product Security systems.· Deep data analysis to solve business problems and to identify business opportunities to provide the best delivery experience on all Amazon-owned sites.· Design, development and evaluation of highly innovative machine learning models for big data.· Analyzing and understanding large amounts of Amazon’s historical business data to detect patterns, to analyze trends and to identify correlations and causalities· Working closely with other software engineering teams to drive real-time model implementations and new feature creations· Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation
US, WA, Seattle
Compensation Science is building economic models and algorithms from the ground up to design and scale pay for hundreds of thousands of Amazon employees worldwide. The fast-growing, interdisciplinary team is working at the intersection of economics, machine learning, and product development.We are looking for an outstanding, end-to-end economist who is able to provide structure to ambiguous, complex business problems, conduct causal econometric analysis, work with engineers to launch science models in product, and measure the impact of policy changes.This role will build and operationalize econometric models for new compensation products owned by the team and initiate high impact projects in the compensation and benefits space. Economists at Amazon have access to engineering and econometric tools and opportunities to learn new econometric and machine learning methods from world-class scholars.Responsibilities:· Own the development of economic models and econometric analysis· Assist in the delivery of automated, scalable analytic models· Work collaboratively with economists, scientists, and engineers on the team and across the HR organization· Interpret and communicate results to global business stakeholders
DE, BY, Munich
As a Research Scientist you will use your experience to develop new strategies to improve the performance of Amazon’s systems and networks. Working closely with fellow research scientists and product managers, you will use your experience in modeling, statistics, and simulation to design models of new policies, simulate their performance, and evaluate their benefits and impacts to cost, reliability, and speed of our fulfillment network.Our teams are looking for experience in network and combinatorial optimization, algorithms, data structures, statistics, and/or machine learning. This position requires superior analytical thinking, and ability to apply their technical and statistical knowledge to identify opportunities for industrial research.You should be able to mine and analyze large data, and be able to use necessary programming and statistical analysis software/tools to do so.
GB, London
As a Research Scientist you will use your experience to develop new strategies to improve the performance of Amazon’s systems and networks. Working closely with fellow research scientists and product managers, you will use your experience in modeling, statistics, and simulation to design models of new policies, simulate their performance, and evaluate their benefits and impacts to cost, reliability, and speed of our fulfillment network.Our teams are looking for experience in network and combinatorial optimization, algorithms, data structures, statistics, and/or machine learning. This position requires superior analytical thinking, and ability to apply their technical and statistical knowledge to identify opportunities for industrial research.You should be able to mine and analyze large data, and be able to use necessary programming and statistical analysis software/tools to do so.
RO, Iasi
We are looking for motivated data scientists with excellent leadership skills, and the ability to develop, automate, and run analytical models of our systems. You will have strong modeling skills and are comfortable owning data and working from concept through to execution. This role will also build tools and support structures needed to analyze data, dive deep into data to resolve root cause of systems errors and changes, and present findings to business partners to drive improvements.Applicants have a demonstrated ability to manage medium-scale modeling projects, identify requirements, and build methodology and tools that are statistically grounded. You will have experience collaborating across organizational boundaries.
GB, London
We are looking for motivated data scientists with excellent leadership skills, and the ability to develop, automate, and run analytical models of our systems. You will have strong modeling skills and are comfortable owning data and working from concept through to execution. This role will also build tools and support structures needed to analyze data, dive deep into data to resolve root cause of systems errors and changes, and present findings to business partners to drive improvements.Applicants have a demonstrated ability to manage medium-scale modeling projects, identify requirements, and build methodology and tools that are statistically grounded. You will have experience collaborating across organizational boundaries.
US, NY, New York
Amazon Web Services (AWS) is building a world-class marketing organization, and we are looking for an Economist to apply the frontier of economic thinking to experimental design, measurement, forecasting, program evaluation, online advertising and other areas. As a key member on the Marketing Data: Science & Engineering (D:SE) team you will partner with a team of experts across the fields of Product Management, Data Science, Machine Learning and Data Engineering to develop new and innovative solutions to some of the hardest challenges in Marketing. In this role, you will work on the key initiatives and partner with business stakeholders, to build solutions and recommendations to drive key business outcomes for the AWS business.About our team:We refuse to accept constraints, internal or external, and have a strong bias for action. We love data and believe that we can use it to deliver epic experiences for our customers. We work across all areas of AWS marketing including our core marketing data solution, insights and reporting, targeting and personalization models, marketing measurement and the operational systems to support each of these areas that power interactions with millions of customers every day. As a multi-functional team of experts, we deliver scaled solutions in use globally across AWS marketing.About you:Economists at Amazon will be expected to work directly with the chief economist and senior management on key business problems faced in retail, international retail, cloud computing, third party merchants, search, Kindle, streaming video, and operations. You will build econometric models, using our world class data systems, and apply economic theory to solve business problems in a fast moving environment. Economists at Amazon will be expected to develop new techniques to process large data sets, address quantitative problems, and contribute to design of automated systems around the company. You have strong leadership qualities, great judgment, clear communication skills, and a track record of shipping great products.
US, VA, Arlington
Amazon’s Global Accounts Receivable team is looking for a Data Scientist to join our fast paced, stimulating environment to help invent the future of Accounts Receivable with technology, and to turn big data into actionable insights.Our team charter is to optimize credit risks, cash flow, customer satisfaction and internal efficiency. We provide insights and recommendations to senior business leaders in terms of policies, process and systems. We build large-scale models that help our global teams manage their receivables portfolios, run their operations to maximum effect and foresee future trends. We contribute algorithms to O2C systems towards effective credit management.We are seeking to hire a Data Scientist with strong scientific acumen, technical skills and communication to join our team.The role will help build global-scale components of our economics and statistical toolkit, initially focusing on trend and regression analysis, machine learning, and more. They will discover and define problems; and find the right quantitative solutions. They will measurably impact the success of the major receivables processes in Amazon's core businesses, including credit and risk management, as well as dunning and collections strategies.The role will actively interact with business in translating requirements into Data Science problem statements; following through modelling and deployment; and driving continuous improvement and learning. The role will work hand in hand with software engineers, business intelligence engineers and business teams towards implementation at scale.Responsibilities· Apply judgement to identify and develop science solutions· Design and develop models to predict process behaviour and outcomes· Apply advanced statistical and/or machine learning know-how e.g. to optimise predictive abilities· Develop new data sources to enable statistical modelling and learning; continuously fine-tune data models· Design and utilise code (Python, R, Scala, etc.) as required· Formulate experiments to assess AR process strategies· Collaborate with engineering to build data, algorithms and models· Communicate scientific solutions and insights effectively to a senior leadership and non-scientific audience
US, CA, Sunnyvale
Interested in making Amazon Echo more intuitive? Help us make Alexa personalized to each of our customers. We’re building the speech and language solutions behind Amazon Echo and other Amazon products and services. Come join us!Alexa is the groundbreaking cloud-based voice service that powers Amazon Echo and other devices designed around your voice. Our mission is to push the envelope in Natural Language Understanding (NLU), Machine Learning (ML), Automatic Speech Recognition (ASR), and Speaker Recognition, in order to provide the best-possible experience for our customers. We’re looking for an Applied Scientist to help build industry-leading speaker recognition technologies and machine learning systems that customers love.The Speaker ID (Voice Recognition) team enables Alexa to provide personalized experiences to millions of Alexa customers. Our mission is to make Alexa your best friend, recognizing you by your voice with confidence. At the core, we use both statistical, deep learning, and neural network models to make the magic happen. We are the brains behind “Alexa, who am I?”, “Echo, call my mom.” and more. We provide millions of Alexa customers personalized experiences 24 hours a day, 7 days a week.As an Applied Scientist for the Alexa Engine team focused on Speaker Recognition, you will be responsible for building industry-leading intelligent offerings that customers love. Our mission is to apply Artificial Intelligence (AI) and Machine Learning (ML), in order to reduce users cognitive load, reduce friction in their day-to-day activities and finally, inspire our customers by enabling serendipitous discovery of experience.We are looking for top Applied Scientists who can build new product and/or 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:· Use machine learning and data analysis to deliver scalable solutions to business problems· Work closely with software engineers on detailed requirements, technical designs and implementation of end-to-end solutions in production· Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving· Research new machine learning approaches to all aspects of the voice recognition, personalization and ASR.You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in artificial intelligence. Your work will directly impact our customers in the form of novel products and services that make use of speech and language technology.
US, MA, North Reading
Working at Amazon RoboticsAre you inspired by invention? Is problem solving through teamwork in your DNA? Do you like the idea of seeing how your work impacts the bigger picture? Answer yes to any of these and you’ll fit right in here at Amazon Robotics. We are a smart team of doers that work passionately to apply cutting edge advances in robotics and software to solve real-world challenges that will transform our customers’ experiences in ways we can’t even image yet. We invent new improvements every day. We are Amazon Robotics and we will give you the tools and support you need to invent with us in ways that are rewarding, fulfilling and fun.Position OverviewThe Amazon Robotics (AR) Virtual Systems Profiling team builds models, runs simulation experiments and delivers analyses that are central to understanding performance of the entire AR system, e.g. operational and software scaling characteristics, bottlenecks, robustness to “chaos monkey” stresses -- we inform critical engineering and business decisions about Amazon’s approach to robotic fulfillment.We seek a talented and motivated engineer to tackle broad challenges in system-level analysis. You will work in a small team to quantify system performance at scale and to expand the breadth and depth of our analysis (e.g. increase the range of software components and warehouse processes covered by our models, develop our library of key performance indicators, construct experiments that efficiently root cause emergent behaviors). You will engage with growing teams of software development and warehouse design engineers to drive evolution of the AR system and of the simulation engine that supports our work.
US, WA, Seattle
Amazon is looking for an outstanding applied scientist to help build next generation selection/assortment systems. On the Core Selection Team within the Supply Chain Optimization Technologies (SCOT) organization, we own the selection for WW Amazon Marketplaces. We build tools and systems that enable our partners and business owners to scale themselves by leveraging our problem domain expertise, focusing instead on introspecting our outputs and iteratively helping us improve our ML models rather than hand-managing their assortment.As an Applied Scientist, you will work with software engineers, product managers and business teams to understand the requirements/current challenges, distill that understanding to elegantly define the problem, and develop innovative solutions to address those problems using techniques in machine learning and optimization.You will work with a team of engineers and scientists who are passionate about using machine learning to build automated systems and solve problems that matter to our customers. Your work will directly impact our customers in form of selection we offer them.Responsibilities:· Research and implement machine learning and optimization models to solve problems that matter to our customers· Understand business requirements and existing challenges and map them to the right scientific solution· Own end-to-end solution in terms of research, prototyping, experimentation to eventual roll-out· Develop the right set of metrics to evaluate efficiency/accuracy of the algorithms· Mentor and develop the scientist community across the organizationTo help describe some of our challenges, we created a short video about Supply Chain Optimization at Amazon - http://bit.ly/amazon-scotAmazon is an Equal Opportunity Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation.
US, NJ, Newark
Are you excited about an opportunity to apply your experience and passion for Machine Learning (ML) and/or Deep Learning (DL) to improving customers product experience? Are you a data scientist who dreams of building scalable solutions and innovations that enhances and enriches millions of customers lives every day? If so, this may be a great fit for you!Audible Product Data Science team partners with technology and product leaders to solve business and technology problems using scientific approaches to build product and services that surprise and delight our customers. Improving Search and Content discovery experience is our key focus. We employ cutting-edge ML, deep learning techniques and Natural Language Processing (NLP) knowledge to improve the relevance of search results, query intents understanding, and recommendation system, etc. We operate in an agile environment in which we own and collaborate the life cycle of research, design, and model development of relevant projects.As a data scientist, you will be responsible for driving projects through their entire lifecycle from idea creation through implementation, experimentation and finally, deployment. You will be working with other data scientists, ML experts, engineers as well as product teams locally and abroad, and on cross-disciplinary efforts with other scientist within Amazon.We are looking for motivated, results-oriented Data Scientist with strong rigor and demonstrable skills in information retrieval, DL, ML, NLP, data mining and/or large-scale distributed computation.
US, WA, Seattle
We’re working on the future. If you are seeking an iterative fast-paced environment where you can drive innovation, apply state-of-the-art technologies to solve extreme-scale real world challenges, and provide visible benefit to end-users, this is your opportunity.Come work on the Prime Air team!We're looking for an outstanding applied scientist who combines superb technical, research and analytical capabilities with a demonstrated ability to get the right things done quickly and effectively. This person must be comfortable working with a team of top-notch applied scientists. We’re looking for people who innovate and love solving hard problems. You will work hard, have fun, and of course, make history!Export License: This position may require a deemed export license for compliance with applicable laws and regulations. Placement is contingent on ’s ability to apply for and obtain an export license on your behalf
US, WA, Seattle
Do you want to join an innovative team of scientists who use machine learning, NLP and statistical techniques to provide the best customer experience on the earth? Do you want to change the way that people work with customer service? Our team wants to lead the technical innovations in these spaces and set the bar for every other company that exists. We love data, and we have lots of it. We're looking for data scientist to own end-to-end business problems and metrics which would have a direct impact on the bottom line of our business while improving customer experience.If you see how big data and cutting-edge technology can be used to improve customer experience, if you love to innovate, if you love to discover knowledge from big structured and unstructured data and if you deliver results, then we want you to be in our team.Major responsibilities· Analyze and extract relevant information from large amounts of both structured and unstructured data to help automate and optimize key processes· Design structured, multi-source data solutions to deliver the dashboards and reports that make data actionable· Drive the collection of new data and the refinement of existing data sources to continually improve data quality· Support data analysts and product managers by turning business requirements into functional specifications and then executing delivery· Lead the technical lifecycle of data presentation from data sourcing to transforming into user-facing metrics· Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation
US, WA, Seattle
Amazon Go is a new kind of store with no lines and no checkout—you just grab and go! Customers simply use the Amazon Go app to enter the store, take what they want from our selection of fresh, delicious meals and grocery essentials, and go! Our checkout-free shopping experience is made possible by our Just-Walk-Out-Technology, which automatically detects when products are taken from the shelves and keeps track of them in a virtual cart. When you’re done shopping, you can just leave the store. Shortly after, we’ll charge your Amazon account and send you a receipt. Check it out at amazon.com/go.We are looking for a seasoned leader to charter continued innovation on behalf of the research science and analytics team at Amazon Go. This role is highly strategic, and this leader will interact with all levels across a broad range of teams across the company.You will lead a team of data scientists, research scientists, applied scientists, economists and engineers in the development of state-of-the-art models to influence a wide range of decisions within Amazon Go spanning across stochastic optimization (i.e., its application in inventory planning, supply chain management, vendor selection) and beyond! You will oversee a team of Business Intelligence Engineers (BIEs) who provide data, self-service tools and analytics to support Amazon Go.The ideal candidate:· Has a track record for developing and applying robust research to business problems· Is entrepreneurial and innovative, and thrives on solving challenging, ambiguous problems· Has built a strong followership, (e.g., a reputation for hiring and developing strong teams)· Is adept at delivering short-term results while also building towards the future· Can work fluently across the research, technical and business aspects of the job· Excels at leadership and stakeholder managementAs the leader of this team, you will:· Work closely with product, business and engineering teams to set the vision and roadmap for your team.· Lead scientists and engineers in the development novel machine learning and optimization models, in addition to their deployment in production systems.· Represent your business and operations to executive leadership across functions.· Identify new mathematical modeling opportunities, and make business cases for resources to pursue the best of them.· Hire, develop, and retain top science and analytic talent.
DE, BY, Munich
Are you a MS or PhD student interested in a 2021 Applied Science Internship in the fields of Speech, Robotics, Computer Vision, or Machine Learning/Deep Learning?Do you enjoy diving deep into hard technical problems and coming up with solutions that enable successful products that improve the lives of people in a meaningful way?If this describes you, come join our research teams at Amazon. As an Applied Science Intern, you will have access to large datasets with billions of images and video to build large-scale machine learning systems. Additionally, you will analyze and model terabytes of text, images, and other types of data to solve real-world problems and translate business and functional requirements into quick prototypes or proofs of concept.We are looking for smart scientists capable of using a variety of domain expertise combined with machine learning and statistical techniques to invent, design, evangelize, and implement state-of-the-art solutions for never-before-solved problems.Machine Learning Science:Amazon has multiple positions available for Machine Learning Scientists in Berlin, Cambridge, Edinburgh, Iasi and Tuebingen.A few of the teams that are hiring include:- Core AI- Amazon Search- AWS AI- Advertising Technologies- Community Shopping- Prime VideoSpeech and Language Technology:We are hiring in all areas of spoken language understanding: ASR, NLP, NLU, text-to-speech (TTS), and Dialog Management. Amazon has multiple positions available for Speech Scientists in Aachen, Barcelona, Berlin, Cambridge, Edinburgh, Gdansk, Haifa, Tel Aviv and Turin.A few of the teams that are hiring currently include:- Alexa ML- Alexa Brain- Alexa Shopping- Amazon Search- CS TechnologyComputer Vision:Amazon has multiple positions available for Computer Vision Scientists in locations such as Berlin, Barcelona, Tuebingen, Haifa and Tel Aviv.We are currently hiring for multiple teams including:· Visual Search· Amazon AI (AWS Rekognition)· Amazon Go· Lab126
DE, BW, Tuebingen
Are you interested in working on fascinating scientific and engineering challenges of modern information technology? Would you like to contribute to the development of the future generation of cloud computing at Amazon Web Services?As a Sr. Applied Scientist, you will be working on cutting edge projects in the intersection of causal inference, machine learning, and high-dimensional statistics. You will be part of an ambitious team of scientists and software engineers that is together developing novel software products for world-wide use.The AWS Causality Lab is located at the Tübingen site in Germany. Our goal is to enable our customers to improve confidence in their data science conclusions by making the underlying cause-effect relationships explicit. Going beyond mere correlational analysis, we quantify the causes of observations, and provide actionable insights based on data-driven what-if predictions.Our mission is to provide automated software for causal inference to our customers which builds on formalisms, algorithms, and statistical guarantees.As a Sr. Applied Scientist in the Causality Lab, you will be responsible for:· research and development of algorithms in causal inference· analyzing different data types, including time series, textual data sources and graphs· infering causal relationships between these inputs, and discriminating these from coincidental correlations· identifying the causes of particularities in data and quantify their specific contributions to downstream metrics· infering interventional and counterfactual analysis· collaborating product and development teams across AWS and Amazon as well as directly with customers· engaging in the interview process and otherwise developing, growing, and mentoring junior scientistsWe 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.
US, NY, New York
Amazon Advertising operates at the intersection of eCommerce and advertising, offering a rich array of digital display advertising solutions with the goal of helping our customers find and discover anything they want to buy. We help advertisers reach Amazon customers on Amazon.com, across our other owned and operated sites, on other high quality sites across the web, and on millions of Kindles, tablets, and mobile devices. We start with the customer and work backwards in everything we do, including advertising. If you’re interested in joining a rapidly growing team working to build a unique, world-class advertising group with a relentless focus on the customer, you’ve come to the right place.In this role you will develop and evaluate machine learning models using large data-sets, cloud services and customer behavior insights to improve our customer’s experience. Working closely with best-in-class engineers you will have the opportunity to apply a variety of machine learning algorithms, including deep learning, and work on one of the world's largest data sets to influence the long term evolution of our technology roadmap.Impact and Career Growth:· Innovate and define the new pricing model for Sponsored Brand across many creative types.· · Addressing principles of allocation function and pricing in ad marketplace auctions;· · Developing efficient algorithms for multi-objective optimization and AI control methods to find operating points for the ad marketplace auctions and to evolve them· · Opportunity to grow and broaden your machine learning skills a make impact – the work you deliver directly impacts customers and revenue!· · Work in an environment that thrives on creativity, experimentation, and product innovation.· · Have the ability to experiment autonomously with meaningful projects.· · Mentor others Applied Scientists.Why you love this opportunity: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.
US, NJ, Newark
Audible is searching for an exceptional data scientist to join our economics team and drive the development of models at the intersection of machine learning and econometrics at scale. The Audible economics organization works across the business to measure and maximize the value Audible delivers to customers, creators, and communities globally. In this role, there will be a focus on partnering with our content and strategy teams to build a groundbreaking catalog of audiobooks and spoken-word entertainment.We are looking for someone experienced in building ML models at scale for complex prediction and optimization problems, who also has a background (or burgeoning interest!) in causal inference or interpretable machine learning. In addition to working with our staff economists, you will also collaborate closely with scientists across Audible and partner teams at Amazon on problems pertinent to subscription businesses and the production of original media content.KEY RESPONSIBILITIES· Work with leadership in our content and strategy organizations to identify key analytical problems and opportunities – your work is expected to be a key input to our future content strategy.· Develop and maintain scalable, innovative data science and machine learning models that deliver actionable insights and results.· Collaborate with other data scientists, economists, and analysts at Audible to build data-driven solutions to key business problems.
US, NJ, Newark
Audible is looking for a talented teammate to join our economics team as senior manager of economic insights. The Audible Economics organization works across the business to measure and maximize the value Audible delivers to customers, creators, and communities globally. In this role, there is a focus on partnering with our economists and data scientists to craft analyses that solve crucial questions for external content stakeholders, including publishers, authors, and the broader creative community of spoken-word entertainment and audiobooks. In addition to helping shape analyses, you will be responsible for translating findings into key, actionable insights, and communicating them clearly to a range of audiences.We are searching for someone who has exceptional presentation, writing, and data visualization skills; a thorough knowledge of analytical methods used in economics, statistics, and data science; and an exemplary ability to tell simple, compelling stories with data. As Audible continues to redefine the ways people access, discover, and share stories, you will be at the forefront of discussions with our content team and creators on how to evolve the industry to discover new value for customers and storytellers alike.KEY RESPONSIBILITIES· Partner with content and strategy leadership to develop a roadmap of key analytical questions for creators.· Conceptualize and deliver analytical, data-driven solutions to those key questions, in original analyses and in partnership with staff economists and data scientists.· Produce data-driven recommendations to augment our content strategy and maximize value for customers and creators.· Develop innovative partnership structures and deal making strategies with existing and new creative partners.