Alexa at five: looking back, looking forward

Today is the fifth anniversary of the launch of the Amazon Echo, so in a talk I gave yesterday at the Web Summit in Lisbon, I looked at how far Alexa has come and where we’re heading next.

Poster-captioned.jpg._CB447972009_.jpg
This poster of the original Echo device, signed by the scientists and engineers who helped make it possible, hangs in Rohit's office.

Amazon’s mission is to be the earth’s most customer-centric company. With that mission in mind and the Star Trek computer as an inspiration, on November 6, 2014, a small multidisciplinary team launched Amazon Echo, with the aspiration of revolutionizing daily convenience for our customers using artificial intelligence (AI).

Before Echo ushered in the convenience of voice-enabled ambient computing, customers were used to searches on desktops and mobile phones, where the onus was entirely on them to sift through blue links to find answers to their questions or connect to services. While app stores on phones offered “there’s an app for that” convenience, the cognitive load on customers continued to increase.

Alexa-powered Echo broke these human-machine interaction paradigms, shifting the cognitive load from customers to AI and causing a tectonic shift in how customers interact with a myriad of services, find information on the Web, control smart appliances, and connect with other people.

Enhancements in foundational components of Alexa

In order to be magical at the launch of Echo, Alexa needed to be great at four fundamental AI tasks:

  1. Wake word detection: On the device, detect the keyword “Alexa” to get the AI’s attention;
  2. Automatic speech recognition (ASR): Upon detecting the wake word, convert audio streamed to the Amazon Web Services (AWS) cloud into words;
  3. Natural-language understanding (NLU): Extract the meaning of the recognized words so that Alexa can take the appropriate action in response to the customer’s request; and
  4. Text-to-speech synthesis (TTS): Convert Alexa’s textual response to the customer’s request into spoken audio.

Over the past five years, we have continued to advance each of these foundational components. In both wake word and ASR, we’ve seen fourfold reductions in recognition errors. In NLU, the error reduction has been threefold — even though the range of utterances that NLU processes, and the range of actions Alexa can take, have both increased dramatically. And in listener studies that use the MUSHRA audio perception methodology, we’ve seen an 80% reduction in the naturalness gap between Alexa’s speech and human speech.

Our overarching strategy for Alexa’s AI has been to combine machine learning (ML) — in particular, deep learning — with the large-scale data and computational resources available through AWS. But these performance improvements are the result of research on a variety of specific topics that extend deep learning, including

  • semi-supervised learning, or using a combination of unlabeled and labeled data to improve the ML system;
  • active learning, or the learning strategy where the ML system selects more-informative samples to receive manual labels;
  • large-scale distributed training, or parallelizing ML-based model training for efficient learning on a large corpus; and
  • context-aware modeling, or using a wide variety of information — including the type of device where a request originates, skills the customer uses or has enabled, and past requests — to improve accuracy.

For more coverage of the anniversary of the Echo's launch, see "Alexa, happy birthday" on Amazon's Day One blog.

Customer impact

From Echo’s launch in November 2014 to now, we have gone from zero customer interactions with Alexa to billions per week. Customers now interact with Alexa in 15 language variants and more than 80 countries.

Through the Alexa Voice Service and the Alexa Skills Kit, we have democratized conversational AI. These self-serve APIs and toolkits let developers integrate Alexa into their devices and create custom skills. Alexa is now available on hundreds of different device types. There are more than 85,000 smart-home products that can be controlled with Alexa, from more than 9,500 unique brands, and third-party developers have built more than 100,000 custom skills.

Ongoing research in conversational AI

Alexa’s success doesn’t mean that conversational AI is a solved problem. On the contrary, we’ve just scratched the surface of what’s possible. We’re working hard to make Alexa …

1. More self-learning

Our scientists and engineers are making Alexa smarter faster by reducing reliance on supervised learning (i.e., building ML models on manually labeled data). A few months back, we announced that we’d trained a speech recognition system on a million hours of unlabeled speech using the teacher-student paradigm of deep learning. This technology is now in production for UK English, where it has improved the accuracy of Alexa’s speech recognizers, and we’re working to apply it to all language variants.

LSTMnetworkanimationV3.gif._CB467045280_.gif
In the teacher-student paradigm of deep learning, a powerful but impractically slow teacher model is trained on a small amount of hand-labeled data, and it in turn annotates a much larger body of unlabeled data to train a leaner, more efficient student model.

This year, we introduced a new self-learning paradigm that enables Alexa to automatically correct ASR and NLU errors without any human annotator in the loop. In this novel approach, we use ML to detect potentially unsatisfactory interactions with Alexa through signals such as the customer’s barging in on (i.e., interrupting) Alexa. Then, a graphical model trained on customers’ paraphrases of their requests automatically revises failing requests into semantically equivalent forms that work.

For example, “play Sirius XM Chill” used to fail, but from customer rephrasing, Alexa has learned that “play Sirius XM Chill” is equivalent to “play Sirius Channel 53” and automatically corrects the failing variant.

Using this implicit learning technique and occasional explicit feedback from customers — e.g., “did you want/mean … ?” — Alexa is now self-correcting millions of defects per week.

2. More natural

In 2015, when the first third-party skills began to appear, customers had to invoke them by name — e.g., “Alexa, ask Lyft to get me a ride to the airport.” However, with tens of thousands of custom skills, it can be difficult to discover skills by voice and remember their names. This is a unique challenge that Alexa faces.

To address this challenge, we have been exploring deep-learning-based name-free skill interaction to make skill discovery and invocation seamless. For several thousands of skills, customers can simply issue a request — “Alexa, get me a ride to the airport” — and Alexa uses information about the customer’s context and interaction history to decide which skill to invoke.

Another way we’ve made interacting with Alexa more natural is by enabling her to handle compound requests, such as “Alexa, turn down the lights and play music”. Among other innovations, this required more efficient techniques for training semantic parsers, which analyze both the structure of a sentence and the meanings of its parts.

Alexa’s responses are also becoming more natural. This year, we began using neural networks for text-to-speech synthesis. This not only results in more-natural-sounding speech but makes it much easier to adapt Alexa’s TTS system to different speaking styles — a newscaster style for reading the news, a DJ style for announcing songs, or even celebrity voices, like Samuel L. Jackson’s.

3. More knowledgeable

Every day, Alexa answers millions of questions that she’s never been asked before, an indication of customers’ growing confidence in Alexa’s question-answering ability.

The core of Alexa’s knowledge base is a knowledge graph, which encodes billions of facts and has grown 20-fold over the past five years. But Alexa also draws information from hundreds of other sources.

And now, customers are helping Alexa learn through Alexa Answers, an online interface that lets people add to Alexa’s knowledge. In a private beta test and the first month of public release, Alexa customers have furnished Alexa Answers with hundreds of thousands of new answers, which have been shared with customers millions of times.

4. More context-aware and proactive

Today, through an optional feature called Hunches, Alexa can learn how you interact with your smart home and suggest actions when she senses that devices such as lights, locks, switches, and plugs are not in the states that you prefer. We are currently expanding the notion of Hunches to include another Alexa feature called Routines. If you set your alarm for 6:00 a.m. every day, for example, and on waking, you immediately ask for the weather, Alexa will suggest creating a Routine that sets the weekday alarm to 6:00 and plays the weather report as soon as the alarm goes off.

Earlier this year, we launched Alexa Guard, a feature that you can activate when you leave the house. If your Echo device detects the sound of a smoke alarm, a carbon monoxide alarm, or glass breaking, Alexa Guard sends you an alert. Guard’s acoustic-event-detection model uses multitask learning, which reduces the amount of labeled data needed for training and makes the model more compact.

This fall, we will begin previewing an extended version of Alexa Guard that recognizes additional sounds associated with activity, such as footsteps, talking, coughing, or doors closing. Customers can also create Routines that include Guard — activating Guard automatically during work hours, for instance.

5. More conversational

Customers want Alexa to do more for them than complete one-shot requests like “Alexa, play Duke Ellington” or “Alexa, what’s the weather?” This year, we have improved Alexa’s ability to carry context from one request to another, the way humans do in conversation.

For instance, if an Alexa customer asks, “When is The Addams Family playing at the Bijou?” and then follows up with the question “Is there a good Mexican restaurant near there?”, Alexa needs to know that “there” refers to the Bijou. Some of our recent work in this area won one of the two best-paper awards at the Association for Computational Linguistics’ Workshop on Natural-Language Processing for Conversational AI. The key idea is to jointly model the salient entities with transformer networks that use a self-attention mechanism.

However, completing complex tasks that require back-and-forth interaction and anticipation of the customer’s latent goals is still a challenging problem. For example, a customer using Alexa to plan a night out would have to use different skills to find a movie, a restaurant near the theater, and a ride-sharing service, coordinating times and locations.

We are currently testing a new deep-learning-based technology, called Alexa Conversations, with a small group of skill developers who are using it to build high-quality multiturn experiences with minimal effort. The developer supplies Alexa Conversations with a set of sample dialogues, and a simulator expands it into 100 times as much data. Alexa Conversations then uses that data to train a bleeding-edge deep-learning model to predict dialogue actions, without the need for a priori hand-authored rules.

State_tracking.png._CB438077172_.png
Dialogue management involves tracking the values of "slots", such as time and location, throughout a conversation. Here, blue arrows indicate slots whose values must be updated across conversational turns.

At re:MARS, we demonstrated a new Night Out planning experience that uses Alexa Conversations technology and novel skill-transitioning algorithms to automatically coordinate conversational planning tasks across multiple skills.

We’re also adapting Alexa Conversations technology to the new concierge feature for Ring video doorbells. With this technology, the doorbell can engage in short conversations on your behalf, taking messages or telling a delivery person where to leave a package. We’re working hard to bring both of these experiences to customers.

What will the next five years look like?

Five years ago, it was inconceivable to us that customers would be interacting with Alexa billions of times per week and that developers would, on their own, build 100,000-plus skills. Such adoption is inspiring our teams to invent at an even faster pace, creating novel experiences that will increase utility and further delight our customers.

1. Alexa everywhere

The Echo family of devices and Alexa’s integration into third-party products has made Alexa a part of millions of homes worldwide. We have been working arduously on bringing the convenience of Alexa, which revolutionized daily convenience in homes, to our customers on the go. Echo Buds, Echo Auto, and the Day 1 Editions of Echo Loop and Echo Frames are already demonstrating that Alexa-on-the-go can simplify our lives even further.

With greater portability comes greater risk of slow or lost Internet connections. Echo devices with built-in smart-home hubs already have a hybrid mode, which allows them to do some spoken-language processing when they can’t rely on Alexa’s cloud-based models. This is an important area of ongoing research for us. For instance, we are investigating new techniques for compressing Alexa’s machine learning models so that they can run on-device.

The new on-the-go hardware isn’t the only way that Alexa is becoming more portable. The new Guest Connect experience allows you to log into your Alexa account from any Echo device — even ones you don’t own — and play your music or preferred news.

2. Moving up the AI stack

Alexa’s unparalleled customer and developer adoption provides new challenges for AI research. In particular, to further shift the cognitive load from customers to AI, we must move up the AI stack, from predictions (e.g., extracting customers’ intents) to more contextual reasoning.

One of our goals is to seamlessly connect disparate skills to increase convenience for our customers. Alexa Conversations and the Night Out experience are the first steps in that direction, completing complex tasks across multiple services and skills.

To enable the same kind of interoperability across different AIs, we helped found the Voice Interoperability Initiative, a consortium of dozens of tech companies uniting to promote customer choice by supporting multiple, interoperable voice services on a single device.

Alexa will also make better decisions by factoring in more information about the customer’s context and history. For instance, when a customer asks an Alexa-enabled device in a hotel room “Alexa, what are the pool hours?”, Alexa needs to respond with the hours for the hotel pool and not the community pool.

We are inspired by the success of learning directly from customers through the self-learning techniques I described earlier. This is an important area where we will continue to incorporate new signals, such as vocal frustration with Alexa, and learn from direct and indirect feedback to make Alexa more accurate.

3. Alexa for everyone

As AI systems like Alexa become an indispensable part of our social fabric, bias mitigation and fairness in AI will require even deeper attention. Our goal is for Alexa to work equally well for all our customers. In addition to our own research, we’ve entered into a three-year collaboration with the National Science Foundation to fund research on fairness in AI.

We envision a future where anyone can create conversational-AI systems. With the Alexa Skills Kit and Alexa Voice Service, we made it easy for developers to innovate using Alexa’s AI. Even end users can build personal skills within minutes using Alexa Skill Blueprints.

We are also thrilled with the Alexa Prize competition, which is democratizing conversational AI by letting university students perform state-of-the-art research at scale. University teams are working on the ultimate conversational-AI challenge of creating socialbots that can converse coherently and engagingly for 20 minutes with humans on a range of current events and popular topics”.

The third instance of the challenge is under way, and we are confident that the university teams will continue to push boundaries — perhaps even give their socialbots an original sense of humor, by far one of the hardest AI challenges.

Together with developers and academic researchers, we’ve made great strides in conversational AI. But there’s so much more to be accomplished. While the future is difficult to predict, one thing I am sure of is that the Alexa team will continue to invent on behalf of our customers.

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

Related content

View from space of a connected network around planet Earth representing the Internet of Things.
Get more from Amazon Science
Sign up for our monthly newsletter

Work with us

See more jobs
US, NY, Virtual Location - New York
The Amazon Economics Team is hiring Interns in Economics. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Some knowledge of econometrics, as well as basic familiarity with Stata or R is necessary, and experience with SQL, UNIX, and Sawtooth would be a plus.These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, data scientists and MBAʼs. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement.Roughly 50% of research assistants from previous cohorts have converted to full time data science or economics employment at Amazon. If you are interested, send your CV, transcripts, and a cover letter to our mailing list at econ-internship@amazon.com.Amazon is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation
US, WA, Seattle
Do you want to join Alexa Artificial Intelligence (AI), 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 real-time metrics monitoring and continuous closed-loop learning. The team owns the modules that reduce user perceived defects and frictions through utterance reformulation, contextual and personalized hypothesis ranking.A day in the lifeAs a Senior Applied Scientist, you will be working alongside a team of experienced machine/deep learning scientists and engineers to create data driven machine learning models and solutions on tasks such as sequence-to-sequence query reformulation, graph feature embedding, personalized ranking, etc..About the hiring groupThe Alexa AI team is in charge of improving Alexa user satisfaction through real-time metrics monitoring and continuous closed-loop learning. The team owns the modules that reduce user perceived defects and frictions through utterance reformulation, contextual and personalized hypothesis ranking.Job responsibilitiesYou will be expected to:· Analyze, understand, and model user-behavior and the user-experience based on large scale data, to detect key factors causing satisfaction and dissatisfaction (SAT/DSAT).· Build and measure novel online & offline metrics for personal digital assistants and user scenarios, on diverse devices and endpoints· Create and innovate deep learning and/or machine learning based algorithms for utterance reformulation and contextual hypothesis ranking to reduce user dissatisfaction in various scenarios;· Perform model/data analysis and monitor user-experienced based metrics through online A/B testing;· Research and implement novel machine learning and deep learning algorithms and models.Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
US, WA, Seattle
The Fresh Food Fast organization is responsible for transforming the online and offline grocery experience for Amazon. We are seeking a senior science leader to define our long-term science vision, build out a high-performing team and deliver business critical scientific models to increase customer engagement, inform long-term investment decisions, and measure how grocery is contributing to Prime and Amazon.A day in the life· You will influence senior leaders (VP+) across business, product, finance, and engineering functions and you will partner closely with central Amazon teams to pioneer new models to measure grocery’s future impact to Prime and Amazon.· You will manage a team of Data Scientists, Economists and BIEs to deliver results on behalf of customersAbout the hiring groupWe’re a team of Product Managers, Data Scientists, Economists and Business Intelligence Engineers focused on deeply understanding how F3 customers engage with physical and online grocery stores in order to enhance their shopping experience, drive engagement and loyalty, and measure their long-term impact to Amazon.Job responsibilitiesYour team will apply complex scientific methods to challenging business problems including, “How can we encourage customers to shop more frequently?”, and “how should we measure the impact of physical store expansion and technology innovation in those stores (e.g. Just Walk Out Technology)?”. You will power through ambiguity, finding the right solutions to problems and influencing others to align with your approach and help drive results. You will mentor and develop scientists to achieve their goals, raising the bar technically and driving scale and efficiencies to better leverage our data and technologies.Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
US, PA, Pittsburgh
Amazon is looking for a passionate, talented, and inventive Scientist with a strong machine learning background to help build industry-leading Speech and Language technology. Our mission is to push the envelope in Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), and Audio Signal Processing, in order to provide the best-possible experience for our customers.As a Scientist, you will work with talented peers to develop novel algorithms and modeling techniques to advance the state of the art in spoken language understanding. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in spoken language understanding.We are hiring in all areas of spoken language understanding: ASR, NLU, text-to-speech (TTS), and Dialog Management.Position Responsibilities:· Participate in the design, development, evaluation, deployment and updating of data-driven models and analytical solutions for machine learning (ML) and/or natural language (NL) applications.· Develop and/or apply statistical modeling techniques (e.g. Bayesian models and deep neural networks), optimization methods, and other ML techniques to different applications in business and engineering.· Routinely build and deploy ML models on available data.· Research and implement novel ML and statistical approaches to add value to the business. Mentor junior engineers and scientists.
US, CA, Sunnyvale
Are you excited about developing state-of-the-art Machine Learning, Computer Vision, Deep Learning and Natural Language Processing algorithms and designs using large data sets to solve real world problems? Do you have proven analytical capabilities and can multi-task and thrive in a fast-paced environment?The Alexa Artificial Intelligence (AI) team is considering an Applied Science Manager who enjoys solving real-world problems that, quite frankly, have not been solved at scale anywhere before. Along the way, you’ll get opportunities to be a fearless and prolific innovator, and a reputed problem solver—someone who truly enables machine learning to create significant impacts.A day in the lifeAs an Applied Science Manager, you will build, lead and inspire a team of scientists and ML engineers solving problems through innovations in deep learning, Computer vision and machine learning. You will take data driven approach coupled with your strong technical expertise in deep learning/computer vision/ML along to lead a team to ship and deliver products. You will be expected to deal with ambiguity, understand business requirements and map them to technical solutions, collaborate with multiple partner teams and be passionate in tackling challenging problems that deliver a great customer experience through innovative solutions.About the hiring groupAre you excited about developing state-of-the-art Machine Learning, Computer Vision, Deep Learning and Natural Language Processing algorithms and designs using large data sets to solve real world problems? Do you have proven analytical capabilities and can multi-task and thrive in a fast-paced environment?Job responsibilitiesAre you excited about developing state-of-the-art Machine Learning, Computer Vision, Deep Learning and Natural Language Processing algorithms and designs using large data sets to solve real world problems? Do you have proven analytical capabilities and can multi-task and thrive in a fast-paced environment?The Alexa Artificial Intelligence (AI) team is considering an Applied Science Manager who enjoys solving real-world problems that, quite frankly, have not been solved at scale anywhere before. Along the way, you’ll get opportunities to be a fearless and prolific innovator, and a reputed problem solver—someone who truly enables machine learning to create significant impacts.Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
US, CA, San Francisco
Prime Video is disrupting traditional media with an ever-increasing selection of movies, TV shows, Emmy Award-winning original content, add-on subscriptions including HBO and Showtime, and live events like Thursday Night Football and Major League Baseball. We are a premier provider of digital entertainment worldwide and we continue to grow very quickly! We need your passion, innovative ideas, and creativity to help continue to deliver on our ambitious goals.How often have you had an opportunity to be a founding member of a team solving significant customer problems through innovative AI technology at Amazon scale? We are looking for passionate, hard-working, and talented individuals to join our fast paced, start-up environment to help invent the future and define the next generation of how customers watch videos.Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by protecting Amazon customers from harmful content? Do you want to build advanced algorithmic systems that help millions of customers every day? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems? If yes, then you may be a great fit to join our Amazon Prime Video team. We are expanding our scene understanding team to drive compliance automation and exceptional customer experience using machine learning, computer vision, audio processing, and natural language understanding. Automation of video understanding at scale is our mission and passion. We need to solve problems across many cultures and languages. we have a huge amount of human-labelled data, and operation team to generate labels across many languages. Our team innovates, with many novel patents, inventions, and papers in the motion picture and television industry. We are highly motivated to extend the state of the art.We embrace the challenges of a fast-paced market and evolving technologies, paving the way to universal availability of content. You will be encouraged to see the big picture, be innovative, and positively impact millions of customers. This is a young and evolving business where creativity and drive will have a lasting impact on the way video is enjoyed worldwide.As a senior applied scientist, you will apply your knowledge of deep learning to concrete problems that have broad cross-organizational, global, and technology impact. Your work will focus on training and evaluating models and deploying them to production where we continuously monitor and evaluate. You will work on large engineering efforts that solve significantly complex problems facing global customers. You will be trusted to operate with independence and are often assigned to focus on areas with significant impact on audience satisfaction. You must be equally comfortable digging in to customer requirements as you are drilling into design with development teams and developing production ready learning models. You consistently bring strong, data-driven business and technical judgment to decisions. This is a greenfield with no "off-the-shelf algorithms" that can perform the job. We experiment a lot and it is a must to learn and be curios. You will be encouraged to see the big picture, be innovative, and positively impact millions of customersYou'll work with experienced managers who'll care for you. We'll guide you on your career growth path and there's no shortage of technical challenges.You will work with internal and external stakeholders, cross-functional partners, and end-users around the world at all levels. Our team makes a big impact because nothing is more important to us than pleasing our customers, continually earning their trust, and thinking long term. You are empowered to bring new technologies and deep learning approaches to your solutions.
US, CA, San Francisco
How often have you had an opportunity to be a founding member of a team solving significant customer problems through innovative AI technology at Amazon scale? We are looking for passionate, hard-working, and talented individuals to join our fast paced, start-up environment to help invent the future and define the next generation of how customers watch videos. We are disrupting a 100-years old industry through cloud services (AWS), 2D/3D computer vision, generative adversarial networks, scalable visual effects (VFX), and machine learning.Prime Video is disrupting traditional media with an ever-increasing selection of movies, TV shows, Emmy Award-winning original content, add-on subscriptions including HBO and Showtime, and live events like Thursday Night Football and Major League Baseball. We are a premier provider of digital entertainment worldwide and we continue to grow very quickly! We need your passion, innovative ideas, and creativity to help continue to deliver on our ambitious goals.We are building a new team to automatically "understand not just tag" the video content on a scene and a frame level by understanding the setting, objects, actions, and themes depicted in a scene. We are driving visual effects automation and exceptional customer experience using machine learning, 2D/3D computer vision, audio processing, and natural language understanding. Automation of video understanding at scale is our mission and passion. We need to solve problems across many cultures and languages. We have a huge amount of human-labelled data, and operation team to generate labels across many languages. Our team innovates, with many novel patents, inventions, and internal/external papers in the motion picture and television industry. We are highly motivated to extend the state of the art.As a senior applied scientist, you will apply your knowledge of deep learning to concrete problems that have broad cross-organizational, global, and technology impact. Your work will focus on training and evaluating models and deploying them to production where we continuously monitor and evaluate. You will work on large engineering efforts that solve significantly complex problems facing global customers. You will be trusted to operate with independence and are often assigned to focus on areas with significant impact on audience satisfaction. You must be equally comfortable digging in to customer requirements as you are drilling into design with development teams and developing production ready learning models. You consistently bring strong, data-driven business and technical judgment to decisions. This is a greenfield with no "off-the-shelf algorithms" that can perform the job. We experiment a lot and it is a must to learn and be curios. We embrace the challenges of a fast paced market and evolving technologies, paving the way to universal availability of content. You will be encouraged to see the big picture, be innovative, and positively impact millions of customers. This is a young and evolving business where creativity and drive will have a lasting impact on the way video is enjoyed worldwide.You'll work with experienced managers who'll care for you. We'll guide you on your career growth path and there's no shortage of technical challenges.You will work with internal and external stakeholders, cross-functional partners, and end-users around the world at all levels. Our team makes a big impact because nothing is more important to us than pleasing our customers, continually earning their trust, and thinking long term. You are empowered to bring new technologies and deep learning approaches to your solutions.
US, CA, San Francisco
Are you interested in revolutionizing the way people around the world enjoy live sports video? Come and join us and be part of the Prime Video Playback team. As a video scientist, you will:· Drive novel live encoding optimization to ensure the best live sports streaming experience delivered to millions of global customers.· Utilize the state-of-the-art computer vision and machine learning techniques to achieve content adaptive live sports encoding to maximize quality per bits at Amazon scale.· Innovate in video quality measurement, video content analysis, and video compression technologies to lead the video industry/community.A day in the lifeAs a video scientist in the Prime Video Playback, you will:· Research and prototype innovative ideas in live sports content analysis, quality measurement, and content-adaptive live video encoding.· Drive technical approach and innovation via proof-of-concept prototyping, paper/report writing, technical presentations and patent filing· Collaborate with and influence product and engineering teams for technology productization and deploymentAbout the hiring groupThe Live Encoding Optimization team's charter is to drive the live streaming video quality improvement at reduced bit costs and low latency, ensuring the best Prime Video customer experience across live sports events and live linear channels. Our innovative technical programs drive benefits at multiple levels: (1) Ensure the best live streaming video quality and Quality-of-Service (QoS) metrics for live events and live linear channel customers, (2) Reduce the live encoding (compute and bit) costs and the associated delivery cost, and (3) Elevate the industry-wide recognition of our innovations in content-driven encoding optimization and video quality measurement.Job responsibilitiesAs a video scientist in the Prime Video Playback, this person shall:· Get familiar with the latest development and advances in video processing, video compression, and computer vision and machine learning to video understanding and analysis· Build research prototypes in live sports video content analysis, objective and subjective video quality measurement, and content-adaptive live video encoding.· Document and present technical proposals and implementations to both internal and external stakeholders and partners.· Work closely with engineering and product team to prioritize technology prototyping, productization and deploymentAmazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
US, WA, Seattle
Amazon 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 a Senior Applied Scientist for the Sponsored Products Detail Page Allocation and Pricing team, you will own 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.As a Senior Applied Scientist on this team you will:· Act as the technical leader in Machine Learning and drive full life-cycle Machine Learning projects.· Develop real-time algorithms to allocate billions of ads per day in advertising auctions.· 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 machine learning approaches.· Recruit Applied Scientists to the team and act as a mentor to other Scientists on the team.Impact and Career Growth:In this role you will have significant impact on this team as well as drive cross team projects that consist of Applied Scientists, Data Scientists, Economists, and Software Development Engineers. This is a highly visible role that will help take our products to the next level. You will work alongside many of the best and brightest science and engineering talent and the work you deliver will have a direct impact on customers and revenue!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.Team video ~ https://youtu.be/zD_6Lzw8raE
US, CA, Sunnyvale
The Alexa Artifical Intelligence (AI) team is looking for a top Applied Scientist who can build new products and help us take our products to the next level using multimodal conversational and embodied AI.A day in the lifeAs a Applied Scientist in Alexa AI, you will perform the duties of a Research Scientist, and also be expected to be strong at implementing the algorithm.About the hiring groupOur mission is to produce value to our customers and delight them using state-of-the-art technology. You will be part of the team who will invent the future and provide magical experiences to our users. Amazon is the most customer-centric company in the world. To get there, we need exceptionally talented, bright, and goal-driven people to work on interactive multimodal learning touching language, vision and audio.Job responsibilities● Research, design, implement, analyze and evaluate novel algorithms for human robotinteraction using multimodal deep learning● Be driven by business goals by applying scientific methods to deliver customer value● Develop state-of-the-art algorithms, contribute to Amazon's Intellectual Property andpublish at top-tier conferences● Collaborate closely with team members on developing systems from prototyping toproduction level● Work closely with software engineering teams to drive Amazon scale, real-timeimplementationsAmazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
US, CA, Sunnyvale
Amazon Lab126 is an inventive research and development company that designs and engineers high-profile consumer electronics. Lab126 began in 2004 as a subsidiary of Amazon.com, Inc., originally creating the best-selling Kindle family of products. Since then, we have produced groundbreaking devices like Fire tablets, Fire TV, and Amazon Echo.The Role:“If you do not work on an important problem, it's unlikely you'll do important work.” – Richard HammingWe have important problems to solve. There are great, world-changing products that should exist, but do not, because the technology to enable them does not exist. Yet. That’s where you come in.We are a smart team of doers that work passionately to apply cutting-edge advances in robotics to solve real-world challenges that will transform our customers’ experiences in ways we can’t even imagine yet. Key responsibilities will be to understand the state of the art, conduct research and develop algorithms by collaborating with cross-functional engineering teams including Amazon Robotics, to put the concepts you develop into production. You will determine where commercially available solution and academic research can be applied to solve Amazon business problems, as well as identify opportunities for innovation. You will use data to train and test algorithms to bring them up to production level quality.If this describes you, come join our team at Lab126 in the heart of Silicon Valley. The team is using probabilistic modeling, machine learning, real-time and distributed systems to convert requirements into concrete deliverables. A researcher on this team will translate business and functional requirements into working code. Comfort with a high degree of ambiguity and ability to solve problems that haven’t been solved to scale before are essential.If you join us, your opportunities will include:· Work on ambiguous problem areas and help formulate the problem statement· Research, design, implement and evaluate novel algorithms· Collaborate closely with team members on developing systems from prototype to production· Work closely with partner engineering teams to drive scalable implementations that can be leveraged by high value customer features
US, CA, San Francisco
The AWS Center for Quantum Computing is a multi-disciplinary team of scientists, engineers, and technicians, all working to innovate in quantum computing for the benefit of our customers. We are looking to hire a Quantum Research Scientist to design and model novel superconducting quantum devices, including qubits and their readout and control schemes. Candidates with a track record of original scientific contributions will be preferred. We are looking for candidates with strong engineering principles, resourcefulness, and a bias for action. Organization and communication skills are essential.Work/Life BalanceAt the AWS CQC, we understand that developing quantum computing technology is a marathon, not a sprint. Mental and physical wellness is encouraged within our team and throughout AWS. The work/life integration within Amazon encourages a culture where employees work hard and have ownership over their downtime. We are exploring more structured wellness elements including for example meditation, running group meet-ups, and other wellness tips.Mentorship and Career GrowthWe are committed to the growth and development of every member of the Center for Quantum Computing. You will receive career-growth-minded management and mentorship from a software and science team and also have the opportunity to participate in Amazon's science mentorship programs.Inclusive and Diverse CultureHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and we host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.
US, WA, Seattle
Are you passionate about the intersection of cloud computing, Big Data, mobile applications, and digital user engagement?We are looking for senior data scientists who long for the opportunity to be a founding member of the data science team for Pinpoint. You will get a unique opportunity to partner with product and engineering to define the data science roadmap - diving deep to understand our customer needs and make sense of their end-user behavior to surface intelligent insights.You will build next generation cloud technologies that leverage big data and machine learning to improve user engagement with mobile apps and web applications. The insights you surface and machine learning models you create will help the world’s leading social media, gaming, sports, educational, and consumer applications.What does it take to succeed in this role?The successful Data Scientist will have extreme bias for action needed in a startup environment, with outstanding leadership skills, proven ability to build and manage medium-scale modeling projects, identify data requirements, build methodology and tools that are statistically grounded.We are seeking someone who can thrive in a fast-paced, high-energy and fun work environment where we deliver value incrementally and frequently. We value highly technical people who know their subject matter deeply and are willing to learn new areas. We look for individuals who know how to deliver results and show a desire to develop themselves, their colleagues, and their career.Our customers are innovators, and you will have the chance to work with them to understand their challenges and design new offerings. Together, we’ll shape not just our own products, but the direction of the industry.About PinpointAmazon Pinpoint makes it easy to run targeted campaigns to improve user engagement. Pinpoint helps you understand your users’ behavior, define who to target, what messages to send, when to deliver them, and tracks the results of the campaign. Pinpoint enables real-time analytics with dashboards for analyzing user engagement, monetization, user demographics, custom events, and funnels so you can understand how users engage with your application.Learn more about our business at https://aws.amazon.com/pinpoint/
US, MA, Cambridge
Alexa is the groundbreaking voice service that powers Echo and other devices designed around your voice. Our team is creating the science and technology behind Alexa. We’re working hard, having fun, and making history. Come join our team! You will have an enormous opportunity to impact the customer experience through design, architecture, and implementation of a cutting edge technology in the products used every day by people you know.We’re looking for a passionate, talented and inventive Applied Science Manager to lead a team of world-class scientists to build industry-leading conversational technologies that customers love. Our team's mission is the enable Alexa to understand sounds and vocalization beyond speech. As an Applied Science Manager, you will· Hire, coach and develop world-class scientists in a dynamic and fast-paced environment· Develop technical vision and roadmaps for new initiatives· Lead a team of talented scientists to work backward from customer experience and develop novel signal processing and machine learning solutions to advance the state of the art in voice technologies.· Impact customer experiences through timely deliveries of innovative product solutions.· Work with our talented scientists to dive deep and solve technical problems on a regular basis· Work with the team to contribute and influence the signal processing and machine learning research outside Amazon· Establish and enhance mechanisms to enable the team to innovate and deliver solutions in an agile and timely manner.· Build strong relationship and collaborate with other teams across Alexa to deliver solutions effectively
US, WA, Seattle
SCOT Network Topology Optimization science team focuses on research areas and tools that determine Amazon outbound transportation network design as we transition to relying on our internal carrier network and accelerate one-day delivery speed. There are various strategic questions the team is attempting to answer, such as: what is the impact of inventory placement on outbound transportation cost and delivery speed? What is the optimal transportation network design given processing capacity constraints? How can we forecast accurately fulfillment pattern for different customer clusters?. If you are interested in diving into a multi-discipline, high impact space this team is for you. So far, we utilized models from various science disciplines such as: Mixed Integer optimization, Random Forest (or other ML techniques), stochastic/probabilistic model, economic analysis, to name a few.In addition to transportation network, we also use forecasting and optimization techniques to evaluate new facilities recommendation for long term estimates, We use machine learning to approximate the network, and simulation of how our choices will perform. The team is a mixture of Software Engineers, Operations Research Scientists, Applied Scientists, Business Intelligence Engineers and Product Managers.We are looking for a Sr. Research Scientist who has a deep knowledge of analyzing large-scale fulfillment data using Machine learning and optimization. Those who are strong in forecasting space should have a breadth of other ML experience in a production environment using techniques. This role will focus on expanding our reach to analyze various fulfillment and transportation for Amazon's supply chain network worldwide.To 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-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation / Age
GB, London
Are you passionate about automated reasoning and program analysis? Do you enjoy diving into the complexity of compilers, interpreters and programming languages? Do you want to enable developers around the world to benefit from automated reasoning tools that are sound, scalable and delightful to use? If so, then we have an exciting opportunity for you. The Automated Reasoning Group in AWS Platform is looking for an Applied Scientist who wants to design and implement the next generation of automated reasoning tools and services.Each day, hundreds of thousands of developers make billions of transactions worldwide on AWS. They harness the power of the cloud to enable innovative applications, websites, and businesses. Using automated reasoning technology and mathematical proofs, AWS allows customers to answer questions about security, availability, durability, and functional correctness. We call this provable security, absolute assurance in security of the cloud and in the cloud. https://aws.amazon.com/security/provable-security/Key responsibilities for this role include:· Invent, implement, and deploy state of the art automated reasoning algorithms and systems for provable-security· Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative and business judgment.· Collaborate with software engineering teams to integrate successful experiments into large scale, highly complex production services.· Report results in a scientifically rigorous way.· Interact with engineers, product managers and related domain experts to dive deep into the types of challenges that we need innovative solutions for.· Work closely with a mentor to expand your career with AWSMentorship & Career GrowthWe have a formal mentor search application that lets you find a mentor that works best for you based on location, job family, job level etc. Your manager can also help you find a mentor or two, because two is better than one. In addition to formal mentors, we work and train together so that we are always learning from one another, and we celebrate and support the career progression of our team members.Inclusion and DiversityOur team is diverse! We drive towards an inclusive culture and work environment. We are intentional about attracting, developing, and retaining amazing talent from diverse backgrounds. Team members are active in Amazon’s 10+ affinity groups, sometimes known as employee resource groups, which bring employees together across businesses and locations around the world. These range from groups such as the Black Employee Network, Latinos at Amazon, Indigenous at Amazon, Families at Amazon, Amazon Women and Engineering, LGBTQ+, Warriors at Amazon (Military), Amazon People With Disabilities, and more.Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.
US, NY, New York
Amazon 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 goals are to help buyers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and to build a major, sustainable business that helps Amazon continuously innovate on behalf of all customers.We are seeking an Applied Science Manager who is an expert in applied Machine Learning, has a deep passion for building data-driven products, an ability to communicate data insights and scientific vision, and has a proven track record of leading both applied scientists and software engineers to execute complex projects and deliver business impacts.As an Applied Science Manager in Machine Learning, you will:· Lead a group of both applied scientists and software engineers to deliver machine-learning and personalization solutions to production.· Advance team's engineering craftsmanship and drive continued scientific innovation as a thought leader and practitioner.· Develop science and engineering roadmap, run Sprint/quarter and annual planning, and foster cross-team collaboration to execute complex projects.· Perform hands-on data analysis, build machine-learning models, run regular A/B tests, and communicate the impact to senior management.· Hire and develop top talents, provide technical and career development guidance to both scientists and engineers in the organization.Impact and Career Growth:You will invent new shopper and advertiser experiences, and accelerate the pace of Machine Learning and Optimization.Influence customer facing shopping experiences to helping suppliers grow their retail business and the auction dynamics that leverage native advertising, this role will be powering the engine of one the fastest growing businesses at Amazon.Define a long-term science vision for our ad marketplace, driven fundamentally from the needs of our customers, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams.This is a role that combines science leadership, organizational ability, technical strength, product focus and business understanding.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.Team video ~ https://youtu.be/zD_6Lzw8raE
CA, BC, Vancouver
Amazon Science gives you insight into the company’s approach to customer focused scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Our scientists continue to publish, teach, and engage with the academic community, in addition to utilizing our working backwards method to enrich the way we live and work.Please visit https://www.amazon.science for more information.Are you interested in helping Amazon ensure that customers make great purchase decisions and that the world's most recognized Brands using Amazon are successful listing and selling their products? The Brand Protection team designs and builds high performance software systems using machine learning that identify and prevent abuse on behalf of brand owners worldwide.We are looking for a highly talented scientist to help build of our vision for Brand Protection. As a applied scientist on the team, you will interface directly with Product and Engineer to build hands of the wheel solutions to determine how Selling Partners (e.g. Third Party Sellers and Retail Vendors) list on our catalog. You will work backwards from data insights and customer feedback to build the right machine learning solutions, and resourceful in finding innovative solutions to unsolved problems.This is a global role that will include interaction with Brands, Sellers and internal teams in countries outside of the United States, requiring a strong ability to communicate effectively and understand the different needs of global customers. You should have extensive experience leading multiple Machine Learning initiatives, from conception to launch in a rapidly evolving environment. Amazon’s growth requires leaders who move fast, have an entrepreneurial spirit to create new solutions, have an unrelenting tenacity to get things done, and are capable of breaking down and solving complex problems.Major responsibilities:· Understand business challenges by analyzing data and customer feedback· Collaborate with tech and product teams on model building strategies and model experiment, implementation and continuous improvement· Analyze and extract relevant information from large amounts of both structured and unstructured data to design strategies to solve business problems.· Use CV, NLP and state-of-the-art machine learning techniques to create scalable solutions for business problems· Create business and analytics reports and present to the senior management teams· Research and implement novel machine learning and statistical approaches
GB, London
Come 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.A day in the lifeTo 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.About the hiring groupPart of the 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 Quality Assurance. 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.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.We strive to be a fast-moving, creative, and high-impact organization, but we think it is equally important to be collaborative, supporting, and high-trust in the way we work. We want to come to work every day loving not only what we do, but who we have the privilege of working with. Come help us make all of this a reality.
US, NY, New York
Amazon 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!The Brand Advertising Measurement (BAM) team strives to reinvent the way advertisers and agencies build brands and drive performance by raising the bar on measurements. Our engineers and scientists are pioneering major innovations at scale and paving new roads in data-driven advertising and measurements. We are using the full suite of AWS services while leveraging Amazon’s unique combination of real-time shopping data and off-Amazon signals.As a Data Scientist on the Brand Advertising Measurement team you will:· Solve real-world problems by analyzing large amounts of business data, diving deep to identify business insights and opportunities, designing simulations and experiments, developing statistical and ML models by tailoring to business needs, and collaborating with Scientists, Engineers, BIE's, and Product Managers.· Utilize code (Python, R, Scala, etc.) to analyze data and build statistical models to solve specific business problems.· Apply statistical or machine learning knowledge to specific business problems and data.· Build decision-making models and propose solution for the business problem you defined· Translate business questions and concerns into specific quantitative questions that can be answered with available data using sound methodologies. In cases where questions cannot be answered with available data, work with engineers to produce the required data.· Deliver with independence on challenging large scale problems with ambiguity.· Retrieve, synthesize, and present critical data in a format that is immediately useful to answering specific questions or improving system performance.· Analyze historical data to identify trends and support decision making.· Improve upon existing methodologies by developing new data sources, testing model enhancements, and fine-tuning model parameters.· Provide requirements to develop analytic capabilities, platforms, and pipelines.· Formalize assumptions about how our systems are expected to work, create statistical definition of the outlier, and develop methods to systematically identify these outliers. Work out why such examples are outliers and define if any actions needed.· Given anecdotes about anomalies or generate automatic scripts to define anomalies, deep dive to explain why they happen, and identify fixes.· Conduct written and verbal presentation to share insights and recommendations to audiences of varying levels of technical sophistication.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