Rohit Prasad, vice president and head scientist for Alexa AI, demonstrates interactive teaching by customers, a new Alexa capability announced last fall.

Alexa: The science must go on

Throughout the pandemic, the Alexa team has continued to invent on behalf of our customers.

COVID-19 has cost us precious lives and served a harsh reminder that so much more needs to be done to prepare for unforeseen events. In these difficult times, we have also seen heroic efforts — from frontline health workers working night and day to take care of patients, to rapid development of vaccines, to delivery of groceries and essential items in the safest possible way given the circumstances.

Communication features.gif
Alexa’s communications capabilities are helping families connect with their loved ones during lockdown.

Alexa has also tried to help where it can. We rapidly added skills that provide information about resources for dealing with COVID-19. We donated Echo Shows and Echo Dots to healthcare providers, patients, and assisted-living facilities around the country, and Alexa’s communications capabilities — including new calling features (e.g., group calling), and the new Care Hub — are helping providers coordinate care and families connect with their loved ones during lockdown.

It has been just over a year since our schools closed down and we started working remotely. With our homes turned into offices and classrooms, one of the challenges has been keeping our kids motivated and on-task for remote learning. Skills such as the School Schedule Blueprint are helping parents like me manage their children’s remote learning and keep them excited about the future.

Despite the challenges of the pandemic, the Alexa team has shown incredible adaptability and grit, delivering scientific results that are already making a difference for our customers and will have long-lasting effects. Over the past 12 months, we have made advances in four thematic areas, making Alexa more

  1. natural and conversational: interactions with Alexa should be as free-flowing as interacting with another person, without requiring customers to use strict linguistic constructs to communicate with Alexa’s ever-growing set of skills. 
  2. self-learning and data efficient: Alexa’s intelligence should improve without requiring manually labeled data, and it should strive to learn directly from customers. 
  3. insightful and proactive: Alexa should assist and/or provide useful information to customers by anticipating their needs.
  4. trustworthy: Alexa should have attributes like those we cherish in trustworthy people, such as discretion, fairness, and ethical behavior.

Natural and conversational 

Accurate far-field automatic speech recognition (ASR) is critical for natural interactions with Alexa. We have continued to make advances in this area, and at Interspeech 2020, we presented 12 papers, including improvements in end-to-end ASR using the recurrent-neural-network-transducer (RNN-T) architecture. ASR advances, coupled with improvements in natural-language understanding (NLU), have reduced the worldwide error rate for Alexa by more than 24% in the past 12 months.

DashHashLM.png
One of Alexa Speech’s Interspeech 2020 papers, “Rescore in a flash: compact, cache efficient hashing data structures for n-gram language models”, proposes a new data structure, DashHashLM, for encoding the probabilities of word sequences in language models with a minimal memory footprint.

Customers depend on Alexa’s ability to answer single-shot requests, but to continue to provide new, delightful experiences, we are teaching Alexa to accomplish complex goals that require multiturn dialogues. In February, we announced the general release of Alexa Conversations, a capability that makes it easy for developers to build skills that engage customers in dialogues. The developer simply provides APIs (application programming interfaces), a list of entity types invoked in the skill, and a small set of sample dialogues that illustrate interactions with the skills’ capabilities. 

Alexa Conversations’ deep-learning-based dialogue manager takes care of the rest by predicting numerous alternate ways in which a customer might engage with the skill. Nearly 150 skills — such as iRobot Home and Art Museum — have now been built with Alexa Conversations, with another 100 under way, and our internal teams have launched capabilities such as Alexa Greetings (where Alexa answers the Ring doorbell on behalf of customers) and “what to read” with the same underlying capability.  

Further, to ensure that existing skills built without Alexa Conversations understand customer requests more accurately, we migrated hundreds of skills to deep neural networks (as opposed to conditional random fields). Migrated skills are seeing increases in understanding accuracy of 15% to 23% across locales. 

Alexa’s skills are ever expanding, with over 100,000 skills built worldwide by external developers. As that number has grown, discovering new skills has become a challenge. Even when customers know of a skill, they can have trouble remembering its name or how to interact with it. 

To make skills more discoverable and eliminate the need to say “Alexa, ask <skill X> to do <Y>,” we launched a deep-learning-based capability for routing utterances that do not have explicit mention of a skill’s name to relevant skills. Thousands of skills are now being discovered naturally, and in preview, they received an average of 15% more traffic. At last year’s International Conference on Acoustics, Speech, and Signal Processing (ICASSP), we presented a novel method for automatically labeling training data for Alexa’s skill selection model, which is crucial to improving utterance routing accuracy as the number of skills continues to grow.  

A constituency tree featuring syntactic-distance measures.
To make the prosody of Alexa's speech more natural, the Amazon Text-to-Speech team uses constituency trees to measure the syntactic distance (orange circles) between words of an utterance, a good indicator of where phrasing breaks or prosodic resets should occur.
Credit: Glynis Condon

As we’ve been improving Alexa’s understanding capabilities, our Text-to-Speech (TTS) synthesis team has been working to increase the naturalness of Alexa’s speech. We have developed prosodic models that enable Alexa to vary patterns of intonation and inflection to fit different conversational contexts. 

This is a first milestone on the path to contextual language generation and speech synthesis. Depending on the conversational context and the speaking attributes of the customer, Alexa will vary its response — both the words chosen and the speaking style, including prosody, stress, and intonation. We also made progress in detecting tone of voice, which can be an additional signal for adapting Alexa’s responses.

Humor is a critical element of human-like conversational abilities. However, recognizing humor and generating humorous responses is one of the most challenging tasks in conversational AI. University teams participating in the Alexa Prize socialbot challenge have made significant progress in this area by identifying opportunities to use humor in conversation and selecting humorous phrases and jokes that are contextually appropriate.

One of our teams is identifying humor in product reviews by detecting incongruity between product titles and questions asked by customers. For instance, the question “Does this make espresso?” might be reasonable when applied to a high-end coffee machine, but applied to a Swiss Army knife, it’s probably a joke. 

We live in a multilingual and multicultural world, and this pandemic has made it even more important for us to connect across language barriers. In 2019, we had launched a bilingual version of Alexa — i.e., customers could address the same device in US English or Spanish without asking Alexa to switch languages on every request. However, the Spanish responses from Alexa were in a different voice than the English responses.  

By leveraging advances in neural text-to-speech (much the way we had used multilingual learning techniques to improve language understanding), we taught the original Alexa voice — which was based on English-only recordings — to speak perfectly accented U.S. Spanish. 

To further break down language barriers, in December we launched two-way language translation, which enables Alexa to act as an interpreter for customers speaking different languages. Alexa can now translate on the fly between English and six other languages on the same device.

In September 2020, I had the privilege of demonstrating natural turn-taking (NTT), a new capability that has the potential to make Alexa even more useful and delightful for our customers. With NTT, Alexa uses visual cues, in combination with acoustic and linguistic information, to determine whether a customer is addressing Alexa or other people in the household — even when there is no wake word. Our teams are working hard on bringing NTT to our customers later this year so that Alexa can participate in conversations just like a family member or a friend.  

Self-learning and data-efficient 

In AI, one definition of generalization is the ability to robustly handle novel situations and learn from them with minimal human supervision. Two years back, we introduced the ability for Alexa to automatically correct errors in its understanding without requiring any manual labeling. This self-learning system uses implicit feedback (e.g., when a customer interrupts a response to rephrase a request) to automatically revise Alexa’s handling of requests that fail. This learning method is automatically addressing 15% of defects, as quickly as a few hours after detection; with supervised learning, these defects would have taken weeks to address. 

Diagram depicting example of paraphrase alignment
We won a best-paper award at last year's International Conference on Computational Linguistics for a self-learning system that finds the best mapping from a successful request to an unsuccessful one, then transfers the training labels automatically.
Credit: Glynis Condon

At December 2020’s International Conference on Computational Linguistics, our scientists won a best-paper award for a complementary approach to self-learning. Where the earlier system overwrites the outputs of Alexa’s NLU models, the newer system uses implicit feedback to create automatically labeled training examples for those models. This approach is particularly promising for the long tail of unusually phrased requests, and it can be used in conjunction with the existing self-learning system.

In parallel, we have been inventing methods that enable Alexa to add new capabilities, intents, and concepts with as little manually labeled data as possible — often by generalizing from one task to another. For example, in a paper at last year’s ACL Workshop on NLP for Conversational AI, we demonstrated the value of transfer learning from reading comprehension to other natural-language-processing tasks, resulting in the best published results on few-shot learning for dialogue state tracking in low-data regimes.

Similarly, at this year’s Spoken Language Technology conference, we showed how to combine two existing approaches to few-shot learning — prototypical networks and data augmentation — to quickly and accurately learn new intents.

Human-like conversational abilities require common sense — something that is still elusive for conversational-AI services, despite the massive progress due to deep learning. We received the best-paper award at the Empirical Methods in Natural Language Processing (EMNLP) 2020 Workshop on Deep Learning Inside Out (DeeLIO) for our work on infusing commonsense knowledge graphs explicitly and implicitly into large pre-trained language models to give machines greater social intelligence. We will continue to build on such techniques to make interactions with Alexa more intuitive for our customers, without requiring a large quantity of annotated data. 

In December 2020, we launched a new feature that allows customers to teach Alexa new concepts. For instance, if a customer says, “Alexa, set the living room light to study mode”, Alexa might now respond, “I don't know what study mode is. Can you teach me?” Alexa extracts a definition from the customer’s answer, and when the customer later makes the same request — or a similar request — Alexa responds with the learned action. 

Alexa uses multiple deep-learning-based parsers to enable such explicit teaching. First, Alexa detects spans in requests that it has trouble understanding. Next, it engages in a clarification dialogue to learn the new concept. Thanks to this novel capability, customers are able to customize Alexa for their needs, and Alexa is learning thousands of new concepts in the smart-home domain every day, without any manual labeling. We will continue to build on this success and develop more self-learning techniques to make Alexa more useful and personal for our customers.

Insightful and proactive

Alexa-enabled ambient devices have revolutionized daily convenience, enabling us to get what we need simply by asking for it. However, the utility of these devices and endpoints does not need to be limited to customer-initiated requests. Instead, Alexa should anticipate customer needs and seamlessly assist in meeting those needs. Smart huncheslocation-based reminders, and discovery of routines are a few ways in which Alexa is already helping customers. 

Illustration of Alexa inferring a customer asking about weather at the beach may be planning a beach trip.
In this interaction, Alexa infers that a customer who asks about the weather at the beach may be interested in other information that could be useful for planning a beach trip.
credit: Glynis Condon

Another way for Alexa to be more useful to our customers is to predict customers’ goals that span multiple disparate skills. For instance, if a customer asks, “How long does it take to steep tea?”, Alexa might answer, “Five minutes is a good place to start", then follow up by asking, "Would you like me to set a timer for five minutes?” In 2020, we launched an initial version of Alexa’s ability to anticipate and complete multi-skill goals without any explicit preprogramming.  

While this ability makes the complex seem simple, underneath, it depends on multiple deep-learning models. A “trigger model” decides whether to predict the customer’s goal at all, and if it decides it should, it suggests a skill to handle the predicted goal. But the skills it suggests are identified by another model that relies on information-theoretic analyses of input utterances, together with subsidiary models that assess features such as whether the customer was trying to rephrase a prior command, or whether the direct goal and the latent goal have common entities or values.  

Trustworthy

We have made significant advances in areas that are key to making Alexa more trusted by customers. In the field of privacy-preserving machine learning, for instance, we have been exploring differential privacy, a theoretical framework for evaluating the privacy protections offered by systems that generate aggregate statistics from individuals’ data. 

At the EMNLP 2020 Workshop on Privacy in Natural Language Processing, we presented a paper that proposes a new way to offer metric-differential-privacy assurances by adding so-called elliptical noise to training data for machine learning systems, and at this year’s Conference of the European Chapter of the Association for Computational Linguistics, we’ll present a technique for transforming texts that preserves their semantic content but removes potentially identifying information. Both methods significantly improve on the privacy protections afforded by older approaches while leaving the performance of the resulting systems unchanged.

Elliptical vs. spherical noise.png
A new approach to protecting privacy in machine learning systems that uses elliptical noise (right) rather than the conventional spherical noise (left) to perturb training data significantly improves privacy protections while leaving the performance of the resulting systems unchanged.


We have also made Alexa’s answers to information-centric questions more trustworthy by expanding our knowledge graph and improving our neural semantic parsing and web-based information retrieval. If, however, the sources of information used to produce a knowledge graph encode harmful social biases — even as a matter of historical accident — the knowledge graph may as well. In a pair of papers presented last year, our scientists devised techniques for both identifying and remediating instances of bias in knowledge graphs, to help ensure that those biases don’t leak into Alexa’s answers to questions.

A two-dimensional representation of our method for measuring bias in knowledge graph embeddings.
A two-dimensional representation of the method for measuring bias in knowledge graph embeddings that we presented last year. In each diagram, the blue dots labeled person1 indicate the shift in an embedding as we tune its parameters. The orange arrows represent relation vectors and the orange dots the sums of those vectors and the embeddings. As we shift the gender relation toward maleness, the profession relation shifts away from nurse and closer to doctor, indicating gender bias.
Credit: Glynis Condon

Similarly, the language models that many speech recognition and natural-language-understanding applications depend on are trained on corpora of publicly available texts; if those data reflect biases, so will the resulting models. At the recent ACM Conference on Fairness, Accountability, and Transparency, Alexa AI scientists presented a new data set that can be used to test language models for bias and a new metric for quantitatively evaluating the test results.

Still, we recognize that a lot more needs to be done in AI in the areas of fairness and ethics, and to that end, partnership with universities and other dedicated research organizations can be a force multiplier. As a case in point, our collaboration with the National Science Foundation to accelerate research on fairness in AI recently entered its second year, with a new round of grant recipients named in February 2021.

And in January 2021, we announced the creation of the Center for Secure and Trusted Machine Learning, a collaboration with the University of Southern California that will support USC and Amazon researchers in the development of novel approaches to privacy-preserving ML solutions

Strengthening the research community

I am particularly proud that, despite the effort required to bring all these advances to fruition, our scientists have remained actively engaged with the broader research community in many other areas. To choose just a few examples:

  • In August, we announced the winners of the third instance of the Alexa Prize Grand Challenge to develop conversational-AI systems, or socialbots, and in September, we opened registration for the fourth instance. Earlier this month, we announced another track of research for Alexa Prize called the TaskBot Challenge, in which university teams will compete to develop multimodal agents that assist customers in completing tasks requiring multiple steps and decisions.
  • In September, we announced the creation of the Columbia Center of Artificial Intelligence Technology, a collaboration with Columbia Engineering that will be a hub of research, education, and outreach programs.
  • In October, we launched the DialoGLUE challenge, together with a set of benchmark models, to encourage research on conversational generalizability, or the ability of dialogue agents trained on one task to adapt easily to new tasks.

Come work with us

Amazon is looking for data scientists, research scientists, applied scientists, interns, and more. Check out our careers page to find all of the latest job listings around the world.

We are grateful for the amazing work of our fellow researchers in the medical, pharmaceutical, and biotech communities who have developed COVID-19 vaccines in record time.

Thanks to their scientific contributions, we now have the strong belief that we will prevail against this pandemic. 

I am looking forward to the end of this pandemic and the chance to work even more closely with the Alexa teams and the broader scientific community to make further advances in conversational AI and enrich our customers’ lives. 

About the Author
Rohit Prasad is VP and head scientist for Alexa AI.

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Job summaryHave you ever wondered how Amazon delivers timely and reliably hundreds of millions of packages to customer’s doorsteps? Are you passionate about data and mathematics, and hope to impact the experience of millions of customers? Are you obsessed with designing algorithmic solutions to very challenging problems?If so, we look forward to hearing from you!Amazon Transportation Services is seeking an Applied Scientist specialized in Operations Research & Machine Learning to be based in the EU Headquarters in Luxembourg. As a key member of the Research Science Team, this person will be responsible for designing algorithmic solutions based on data and mathematics for optimizing the middle-mile Amazon Transportation Network. The successful applicant will ensure that our end-to-end strategies in terms of customer demand fulfillment, routing, consolidation locations, linehaul/airhaul/sea options and last-mile transportation are streamlined and optimized.This critical role requires an aptitude for independent initiative and decision-making, the ability to drive innovation in modelling and optimization across Amazon’s expanding European network and linking into global initiatives and expansion strategies. Key job responsibilities• Partner with the planning, linehaul/airhaul and sort center operations teams, while working closely with last-mile, supply chain, and global delivery departments for modeling and optimizing the transportation network of EU.• Design and prototype algorithmic solutions for standardized processes.• Lead complex time-bound, long-term as well as ad-hoc transportation modelling analyses to help management in decision making.• Communicate to leadership results from business analysis, strategies and tactics.• Drive large-scale projects to scale and enhance Amazon’s EU transportation network.
IN, TS, Hyderabad
Are you interested in applying your strong quantitative analysis and big data skills to world-changing problems? Are you interested in driving the development of methods, models and systems for strategy planning, transportation and fulfillment network? If so, then this is the job for you.Our team is responsible for creating core analytics tech capabilities, platforms development and data engineering. We develop scalable analytics applications and research modeling to optimize operation processes. We standardize and optimize data sources and visualization efforts across geographies, builds up and maintains the online BI services and data mart. You will work with professional software development managers, data engineers, scientists, business intelligence engineers and product managers using rigorous quantitative approaches to ensure high quality data tech products for our customers around the world, including India, Australia, Brazil, Mexico, Singapore and Middle East.We are looking for experienced hands-on Manager of Data Science to join us and lead science programs and developments for Amazon global.Amazon is growing rapidly and because we are driven by faster delivery to customers, a more efficient supply chain network, and lower cost of operations, our main focus is in the development of analytics tech services and applications fed by our massive amounts of available data. You will be responsible for building these models/tools that improve the economics of Amazon’s worldwide fulfillment networks in emerging countries as Amazon increases the speed and decreases the cost to deliver products to customers. You will identify and evaluate opportunities to reduce variable costs by improving fulfillment center processes, transportation operations and scheduling, and the execution to operational plans. You will also improve the efficiency of capital investment by helping the fulfillment centers to improve storage utilization and the effective use of automation. Finally, you will help create the metrics to quantify improvements to the fulfillment costs (e.g., transportation and labor costs) resulting from the application of these optimization models.Major responsibilities of the team include:· Translating business questions and concerns into specific analytical questions that can be answered with available data using statistical methods.· Apply Statistical and Machine Learning methods to specific business problems and data.· Ensure data quality throughout all stages of acquisition and processing, including such areas as data sourcing/collection, ground truth generation, normalization, transformation, cross-lingual alignment/mapping, etc.· Communicate proposals and results in a clear manner backed by data and coupled with actionable conclusions to drive business decisions.· Collaborate with colleagues from multidisciplinary science, engineering and business backgrounds.· Work with engineers to develop efficient data querying and modeling infrastructure.· Manage your own process. Prioritize and execute on high impact projects, triage external requests, and ensure to deliver projects in time.· Utilizing code (Python, R, Scala, etc.) for analyzing data and building statistical models.
US, WA, Seattle
Job summaryAmazon Advertising is one of Amazon's fastest growing and most profitable businesses. As a core product offering within our advertising portfolio, Sponsored Products (SP) helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The SP team's primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day!As a Senior Applied Scientist on this team, you will:Be a technical leader in Machine Learning and drive full life-cycle Machine Learning projects.Lead technical efforts within this team and across other teams.Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production.Run A/B experiments, gather data, and perform statistical analysis.Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving.Work closely with software engineers to assist in productionizing your ML models.Research new and innovative machine learning approaches.Recruit Applied Scientists to the team and mentor scientists on the team.Why you will love this opportunity: Amazon is investing heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate.Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding.Team video https://youtu.be/zD_6Lzw8raE
US, WA, Seattle
The GSF (Global Specialty Fulfillment) organization leads the innovation of Amazon’s ultra-fast fulfillment initiatives. We are an Operations org that hires and manages associates for ultra-fast businesses such as online grocery delivery, sub-same day delivery etc. GSFTech sits within GSF with the mission to build world-class automated Science-Tech products that enable ultra-fast delivery speeds for Amazon customers and job market opportunities for Amazon associates. Our key vision is to transform the online experience. We’re growing in scale and volume, by orders of magnitude. We are a team of passionate tech builders who work endlessly to make life better for our associates through amazing, thoughtful, and creative new scheduling experiences. To succeed, we need senior technical leaders to forge a path into the future by building innovative, maintainable, and scalable systems.At Amazon, we are constantly inventing and re-inventing to be the most associate-centric company in the world. To get there, we need exceptionally talented, bright, and driven people. Amazon is one of the most recognizable brand names in the world and we distribute millions of products each year to our loyal customers.We are looking for an Operational Research Scientist who will be driving optimization initiatives, responsible for building models and prototypes for labor planning systems, and will require close collaboration with other scientists on the team that are developing state-of-the-art ML and forecasting algorithms to scale. Common questions include: when to post shifts given changing demand and associate acceptance? How to optimally assign shifts to associates? This team plays a significant role in various stages of the innovation pipeline from identifying business needs, developing new algorithms, prototyping/simulation, to implementation by working closely with colleagues in engineering, product management, operations, retail and finance.As a Senior member of the scientist team, you will play an integral part on our Operations org with the following technical and leadership responsibilities:· Interact with engineering, operations, science and business teams to develop an understanding and domain knowledge of processes, system structures, and business requirements· Apply domain knowledge and business judgment to identify opportunities and quantify the impact aligning research direction to business requirements and make the right judgment on research project prioritization· Develop scalable models to derive optimal or near-optimal solutions to existing and new scheduling challenges· Create prototypes and simulations to test devised solutions· Advocate technical solutions to business stakeholders, engineering teams, as well as executive-level decision makers· Work closely with engineers to integrate prototypes into production system· Create policy evaluation methods to track the actual performance of devised solutions in production systems, identify areas with potential for improvement and work with internal teams to improve the solution with new features· Mentor and supervise the work of junior scientists on the team for technical development and their career development and growth· Present business cases and document models, analyses, and their results in order to influence important decisions
US, WA, Seattle
The GSF (Global Specialty Fulfillment) organization leads the innovation of Amazon’s ultra-fast fulfillment initiatives. We are an Operations org that hires and manages associates for ultra-fast businesses such as online grocery delivery, sub-same day delivery etc. GSFTech sits within GSF with the mission to build world-class automated Science-Tech products that enable ultra-fast delivery speeds for Amazon customers and job market opportunities for Amazon associates. Our key vision is to transform the online experience. We’re growing in scale and volume, by orders of magnitude. We are a team of passionate tech builders who work endlessly to make life better for our associates through amazing, thoughtful, and creative new scheduling experiences. To succeed, we need senior technical leaders to forge a path into the future by building innovative, maintainable, and scalable systems.At Amazon, we are constantly inventing and re-inventing to be the most associate-centric company in the world. To get there, we need exceptionally talented, bright, and driven people. Amazon is one of the most recognizable brand names in the world and we distribute millions of products each year to our loyal customers.We are looking for a Senior Applied Scientist who will be the science lead for all key ML and forecasting initiatives, responsible for building models and prototypes for labor planning systems, and will require close collaboration with other scientists on the team that are developing state-of-the-art optimization algorithms to scale. This team plays a significant role in various stages of the innovation pipeline from identifying business needs, developing new algorithms, prototyping/simulation, to implementation by working closely with colleagues in engineering, product management, operations, retail and finance.As a Senior member of the scientist team, you will play an integral part on our Operations org with the following technical and leadership responsibilities:· Help the team define the forward looking Science roadmap and vision by helping to identify, disambiguate and seek out new opportunities· Interact with engineering, operations, science and business teams to develop an understanding and domain knowledge of processes, system structures, and business requirements· Apply domain knowledge and business judgment to identify opportunities and quantify the impact aligning research direction to business requirements and make the right judgment on research project prioritization· Develop scalable models to derive optimal or near-optimal solutions to existing and new scheduling challenges· Create prototypes and simulations to test devised solutions· Advocate technical solutions to business stakeholders, engineering teams, as well as executive-level decision makers· Work closely with engineers to integrate prototypes into production system· Create policy evaluation methods to track the actual performance of devised solutions in production systems, identify areas with potential for improvement and work with internal teams to improve the solution with new features· Mentor and supervise the work of junior scientists on the team for technical development and their career development and growth· Present business cases and document models, analyses, and their results in order to influence important decisions
CA, ON, Toronto
Job summaryAmazon Advertising is one of Amazon's fastest growing and most profitable businesses. As a core product offering within our advertising portfolio, Sponsored Products (SP) helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The SP team's primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day!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.As an Applied Scientist for the Sponsored Products Detail Page Allocation and Pricing team, you develop systems which make the final decision on which ads to show, where to place them on the page and how many ads to place. This also includes selection of various themes that would appear in detail pages. This is a challenging technical and business problem, which requires us to balance the interests of advertisers, shoppers, and Amazon. You'll develop a data-driven product strategy to define the right quantitative measures of shopper impact, using this to evaluate decisions and opportunities. You'll balance a portfolio of pragmatic and long-term investments to drive long term growth of the ads and retail businesses. You'll develop real-time algorithms to allocate billions of ads per day in advertising auctions.As an Applied Scientist on this team you will:Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production.Run A/B experiments, gather data, and perform statistical analysis.Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving.Work closely with software engineers to assist in productionizing your ML models.Research new machine learning approaches.Why you love this opportunityAmazon is investing heavily in building a world-class advertising business. This team is responsible for defining and delivering a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate.Impact and Career GrowthYou will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven fundamentally from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding.Team video https://youtu.be/zD_6Lzw8raE
US, WA, Seattle
Job summaryDo you want to join the Alexa Artificial Intelligence (AI) team - the science team behind Amazon’s intelligence voice assistance system? Do you want to utilize cutting-edge deep-learning and machine learning algorithms to delight millions of Alexa users around the world?If your answers to these questions are “yes”, then come join the Alexa AI team, which is in charge of improving Alexa user satisfaction through continuous closed-loop self-learning. The team owns the modules that reduce user perceived defects through automatic defect detection and label generation.Key job responsibilitiesYou will be expected to:· Analyze, understand, and model dialogue context based on large scale speech and dialogue data;· Create and innovate deep learning and/or NLP based algorithms for improving accuracy of Alexa's speech recognition and natural language understanding through contextual modeling;· Perform model/data analysis and monitor user-experienced based metrics through online A/B testing;· Research and implement novel deep learning and NLP algorithms and models.A day in the life· Work collaboratively with scientists and developers to design and implement automated, scalable NLP/ML/IR models for accessing and presenting information· Drive scalable solutions from the business, to prototyping, production testing and through engineering directly to production· Drive best practices on the team, deal with ambiguity and competing objectives, and mentor and guide junior members to achieve their career growth potential.About the teamThe Alexa AI team is in charge of improving Alexa user satisfaction through continuous closed-loop self-learning. The team owns the modules that reduce user perceived defects through automatic defect detection and label generation.You will be working alongside a team of experienced deep learning and NLP scientists and engineers to create deep neural network based contextual dialogue modeling on tasks such as speech translation, natural language understanding, etc.
GB, London
Job summaryCome build the future of entertainment with us. Are you interested in shaping the future of movies and television? Do you want to define the next generation of how and what Amazon customers are watching?Prime Video is a premium streaming service that offers customers a vast collection of TV shows and movies - all with the ease of finding what they love to watch in one place. We offer customers thousands of popular movies and TV shows from Originals and Exclusive content to exciting live sports events. We also offer our members the opportunity to subscribe to add-on channels which they can cancel at anytime and to rent or buy new release movies and TV box sets on the Prime Video Store. Prime Video is a fast-paced, growth business - available in over 240 countries and territories worldwide. The team works in a dynamic environment where innovating on behalf of our customers is at the heart of everything we do. If this sounds exciting to you, please read on.As part of the Prime Video Automated Excellence organization, the Automated Reasoning team applies deep and cutting-edge automated reasoning techniques to detect defects automatically in Prime Video’s core systems and device-level code. The tools we build are mission-critical to the software development and release cycle of many Prime Video engineering organizations, and will represent a huge step forward in the sophistication of our approach to automated software quality. Your work on this team will help us address a new dimension of scale our business faces as we deliver our applications on an ever-expanding set of client devices.Key job responsibilitiesYou will have the opportunity to apply your deep knowledge of automated reasoning techniques, such as static analysis, formal verification, symbolic execution, etc., to concrete problems our product and engineering teams face on a daily basis. You will collaborate with team members to design and deliver enterprise-scale systems that will be used by both internal and external customers. You will have the opportunity to analyse and verify code to solve real-world problems and translate business and functional requirements into quick prototypes or proofs of concept. You will help set and continuously evolve a culture of innovation and curiosity that helps us find and solve our customers’ biggest problems.About the teamTo help a growing organization quickly deliver more features to Prime Video customers, Prime Video’s Automated Excellence organization is innovating on behalf of our global software development team consisting of thousands of engineers. We build services and utilities that make developer’s lives easier and more productive, and that help them deliver at higher levels of quality.
US, CA, Sunnyvale
Job summaryThe Amazon Alexa app is a companion to Alexa devices for setup, remote control, and enhanced features. The Alexa app understands a customer’s habits, preferences and delivers a personalized experience to help them manage their day by providing relevant information as customers want it. We believe voice is the most natural user interface for interacting with technology across many domains; we are inventing the future. As voice-enabled technology becomes increasingly advanced, consumers are demanding more from what their voice products can do. We’re looking for Scientists who are passionate about innovating on behalf of customers, demonstrate a high degree of product ownership, and want to have fun while they make history.As an Senior Data Scientist, you will help build a production scaled personalized recommendation and lead the team to build Machine Learning (ML) and Deep Learning (DL) models to help derive business value and new insights through the adoption of Artificial Intelligence (AI).Key job responsibilitiesThe successful candidate will be responsible for distilling user data insights for ML science applications and influence business decision with data-driven approach to increase Alexa mobile engagement and growth. A successful candidate will be a person who enjoys diving deep into data, doing analysis, discovering root causes, and designing long-term solutions.· Define the long-term development, science and business strategies for the team.· Expertise in the areas of data science, machine learning and statistics.· Translate business needs into advanced analytics and machine learning models and provide strong algorithm and coding execution and delivery of Machine Learning & Artificial Intelligence.· Work closely with the engineers to architect and develop the best technical design and approach.· Being able to dive a ML / DL project from beginning to end, including understanding the business need, aggregating data, exploring data, building & validating predictive models, and deploying completed models to deliver business impact to the organization.· Analyze, extract, normalize, and label relevant data.· Work with Engineers to help our customers operationalize models after they are built.A day in the life· Design and review mobile experiments for growth and engagement· Build statistical models and generate data insights to understand mobile growth and retention· Feature engineering to improve ML model performance.· Analyze, extract, normalize, and label relevant data.· Work with Engineers to deploy applications to production· Work with product manager to convert business problems to science problems and define the solutions.About the teamAlexa Mobile Intelligence team is motivated to make Alexa mobile app being the best intelligent assistant and providing personalized relevant features and content by understanding customers' habits, preferences, hence will reach high growth and retention for the app.
US, CA, San Diego
Job summaryAmazon.com strives to be Earth's most customer-centric company where people can find and discover anything they want to buy online. We hire the world's brightest minds, offering them a fast paced, technologically sophisticated and friendly work environment.Economists at Amazon will be expected to work directly with senior management on key business problems faced in retail, international retail, cloud computing, third party merchants, search, Kindle, streaming video, and operations. Amazon economists will apply the frontier of economic thinking to market design, pricing, forecasting, program evaluation, online advertising and other areas. 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.Key job responsibilitiesAt Customer Trust and Partner Support, we work hard to build trust with both Amazon Customers and Selling Partners. The role offers a unique opportunity to work in a fast growing business at an intersection between Customers and Selling Partners. You will work on a central economist team focused on building scalable analytical solutions for shaping the strategies of the business.We are looking for a seasoned Economist who has a deep and broad analytic background, and is passionate about helping business to make data- and science-based decisions to make a real difference. Experience in applied analytics is essential, and you should be familiar with modern tools for data science and business analysis. We seek creative thinkers and practical doers who can think big and who can also roll up sleeves and execute on these big ideas.A day in the lifeAs a senior economist on the team, you will partner with our business and engineering partners to deliver innovative modeling solutions to help Selling partners and Brands to thrive on Amazon and to ensure a smooth and trustworthy experience for our Customers. This is an opportunity for a high-energy individual to bring economics and Machine Learning techniques into real world applications, to build scalable solutions to systematically deliver impacts to millions of Customers and Selling Partners, and to communicate insights to business and leadership.About the teamAt Selling Partner Risk Econ team, Economists and scientists create science- and data-based solutions at scale for evaluating and optimizing programs for safeguarding consumers against potentially risky sellers and products, and for designing mechanisms to encourage good behavior and deter bad behavior of Amazon sellers.
US, CA, Long Beach
Amazon.com strives to be Earth's most customer-centric company where people can find and discover anything they want to buy online. We hire the world's brightest minds, offering them a fast paced, technologically sophisticated and friendly work environment.Economists at Amazon will be expected to work directly with our Chief Economists and senior management on key business problems faced in retail, international retail, cloud computing, third party merchants, search, Kindle, streaming video, and operations. Amazon economists will apply the frontier of economic thinking to market design, pricing, forecasting, program evaluation, online advertising and other areas. 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.This role will work in Global Shipping, Our vision is to become the carrier of choice for all of our Selling Partners cross-border shipping needs, offering complete set of end to end cross border solutions from key manufacturing hubs to footprint countries supporting business who use Amazon to grow their business globally.The Global Mile Business Development and strategy team are responsible for growing our global shipping product over the coming years. We have Sales and Marketing, analytical, strategy and forecasting functions and work with a large product and tech team, globally.As we expand, the need for robust demand forecasting to aid decision making on asset utilization especially where we know demand will be variable becomes vital. We are investing in enhanced data and improved modelling techniques, and are now in need of an experienced economist to lead and manage our forecasting and scenario planning function.