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.

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.

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.

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.

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The Amazon Devices team designs and engineers consumer electronics, including the best-selling Kindle family of products, Fire tablets, Fire TV, Amazon Dash, and Amazon Echo.As an Applied Scientist, you will participate in the design, development, and evaluation of models and machine learning (ML) technology to delight our customers.You will be part of a team delivering features that are well received by our customers.
US, WA, Seattle
Workforce Staffing (WFS) supports Amazon Operations by hiring the hourly associates that staff our operational buildings. WFS is quickly becoming one of the world’s largest staffing organizations, forecasted to hire over one million hourly associates across North America and the European Union this year alone. Currently, we hire full time, part time, flex time and seasonal hires across Fulfillment Centers, Sort Centers, Amazon Logistics, Whole Foods, Amazon Air, Prime Now, Amazon Fresh, and emerging business lines. Interested in the businesses that Amazon creates and grows? Here’s your opportunity to be a part of this journey.The Workforce Intelligence team was created in 2018 to support the massive growth in scale and scope that WFS has experienced. The team has continued to grow rapidly in order to meet the expanding needs of the business, including: big data and machine learning solutions, innovative approaches to complex HR problems, and data-driven recommendations during a time of rapid change.Here’s where you come in:As a Research Scientist in Workforce Intelligence, your work is focused on research to deeply understand the people that make up our hourly workforce and help others do the same. You understand that even when hiring hundreds of thousands of hourly associates across multiple types of roles and businesses, the experience of each candidate matters.You use your deep expertise in surveys and statistics (regressions, multilevel models, etc.) to define and answer nebulous problems. You use experimental, quasi-experimental, and RCT methods to understand our candidates and influence critical business decisions. You relentlessly obsess over understanding our candidates and lead our survey program that seeks to amplify the voice of our candidates. You work with colleagues across Research, Data Science, Business Intelligence and related teams to enable Amazon find and hire the right candidates for the right roles at an unprecedented scale.This will be a highly visible role across multiple key deliverables for our global organization. If you are passionate and curious about data, obsess over customers, love questioning the status quo, and want to make the world a better place, let’s chat.
US, WA, Seattle
Amazon Science gives you insight into the company’s approach to customer-obsessed 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 for more information.At Alexa Shopping, we strive to enable shopping in everyday life. We allow customers to instantly order whatever they need, by simply interacting with their Smart Devices such as Amazon Show, Spot, Echo, Dot or Tap. Our Services allow you to shop, no matter where you are or what you are doing, you can go from 'I want that' to 'that's on the way' in a matter of seconds. We are seeking the industry's best to help us create new ways to interact, search and shop. Join us, and you'll be taking part in changing the future of everyday life.What you will do: You will lead a team of talented and experienced scientists and engineers that implement solutions for natural language understanding of Alexa Shopping customers: this involves taking the outputs from automated speech recognition (ASR) component and producing a representation of its meaning. Additionally, your team will build Alexa Shopping Automated CX quality metrics and provide analytics. This involves exploring, developing, socializing, and implementing mechanisms for tracking automated CX quality across customer’s journey with Alexa Shopping to improve Alexa Shopping CX. And finally, you will have the satisfaction of being able to look back and say you were a key contributor to something special from its earliest stages. You will be working closely with executive leadership, multiple product managers and leaders from partner teams in Amazon Retail, Alexa, and Speech Recognition teams.What we are looking for: We are looking for a talented Data Science Manager with a strong technical background and solid people management skills to build, manage and develop a highly-talented and experienced data science team. We are seeking leaders that can guide technical and product innovation in the areas of voice experiences, machine learning models and the distributed systems to bring our vision together. Strong judgment and communication skills, long term technical vision, and continuous focus on engineering and operational excellence are essential for the success in this role.
DE, BE, Berlin
As a Senior Applied Scientist on this growing team, you will take on a key role in improving the NLP and ranking capabilities of the Amazon product search engine. Our ultimate goal is to help customers find the products they are searching for, and discover new products they would be interested in. We do so by developing NLP components that cover a wide range of languages, not only English and major languages of Europe, but also Turkish, Arabic, Japanese, and more. The team plays a central role in search query understanding, product indexing, and representations/embeddings of queries and products, all of which aid in improving the ranking and relevance of search results.This is a rewarding role where you will be able to draw a clear connection between your work and how it improves the experience of millions of Amazon customers across the globe every day. You will propose and explore publication-worthy innovation in NLP and IR to build ML models trained on terabytes of product and traffic data, which are evaluated using both offline metrics as well as online metrics from A/B testing. You will then integrate these models into the production search engine that serves customers, closing the loop through data, modeling, application, and customer feedback. The chosen approaches for model architecture will balance business-defined performance metrics with the needs of millisecond response times.Your responsibilities include:· Analyze the data and metrics resulting from traffic into Amazon's product search service· Design, build, and deploy effective and innovative ML solutions to improve various components of the search stack, such as indexing, ranking, and query understanding· Evaluate the proposed solutions via offline benchmark tests as well as online A/B tests in production· Publish and present your work at internal and external scientific venues in the fields of ML/NLP/IRYour benefits include:· Working on a high-impact, high-visibility product, with your work improving the experience of millions of customers· The opportunity to use (and innovate) state-of-the-art ML methods to solve real-world problems· Being part of a growing team where you can influence the team's mission, direction, and how we achieve our goals· Excellent opportunities, and ample support, for career growth, development, and mentorship· Competitive compensation, including relocation support (for both domestic and international candidates)
US, WA, Seattle
Business/Team IntroductionThe Supply Chain Optimization Technologies (SCOT) team builds technology to automate and optimize Amazon’s supply chain of physical goods. We seek a Data Scientist with strong analytical and communication skills to join our team. SCOT manages Amazon's inventory under uncertainty of demand, pricing, promotions, supply, vendor lead times, and product life cycle. We optimize complex trade-offs between customer experience, inventory costs, fulfillment costs, fulfillment center capacity, etc. We develop sophisticated algorithms that involve learning from large amounts of data such as prices, promotions, similar products, and other data from our product catalog in order to automatically act on millions of dollars’ worth of inventory weekly and establish plans for tens of thousands of employees. As a Data Scientist, you will contribute to the research community, by working with other scientists across Amazon and our Supply Chain, as well as collaborating with academic researchers and publishing papers. SCOT also engages in cutting edge research that we try to share with the community. Recent work from SCOT includes papers presented at the NIPS 2017 Time Series Workshop, SSRN, KDD 2018 Time Series Workshop, and ICML 2018 Deep Generative Models Workshop.Data Scientist ResponsibilitiesAs a Data Scientist in SCOT, will be tasked to understand and work with bleeding edge research to enable the implementation of sophisticated models on big data. As a successful data scientist in the SCOT team, you are an analytical problem solver who enjoys diving into data from various businesses, is excited about investigations and algorithms, can multi-task, and can credibly interface between scientists, engineers and business stakeholders. Your expertise in synthesizing and communicating insights and recommendations to audiences of varying levels of technical sophistication will enable you to answer specific business questions and innovate for the future.Major responsibilities include:· Analysis of large amounts of data from different parts of the supply chain and their associated business functions· Improving upon existing machine learning methodologies by developing new data sources, developing and testing model enhancements, running computational experiments, and fine-tuning model parameters for new models· Formalizing assumptions about how models are expected to behave, creating definitions of outliers, developing methods to systematically identify these outliers, and explaining why they are reasonable or identifying fixes for them· Communicating verbally and in writing to business customers with various levels of technical knowledge, educating them about our research, as well as sharing insights and recommendations· Utilizing code (Python, R, Scala, etc.) for analyzing data and building statistical and machine learning models and algorithms
US, WA, Seattle
Global Talent Management (GTM) is centrally responsible for creating and evolving Amazon’s human capital and talent programs and processes.People Science Team within GTM is a growing start-up team with direct impact on Amazonians across all of our businesses and locations around the world. We play a crucial role in ensuring top notch data products and insights facilitate our growth and development of talent in intelligent and curious ways. We regularly use data to pitch ideas and drive conversations with Amazon’s Senior Vice President of HR and other executives about how to improve existing talent programs to solve organizational problems focused on (but not limited to) talent differentiation, talent movement, employee-role matching, product integration, promotion practices, organization design and succession planning, and diversity and inclusion, or invent new ones that address the evolving needs of our diverse employee base.We are looking for a self-driven Economist to help shape analytics and research roadmap and enable data-driven innovation that fuel our rapidly scaling talent management mission. 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 GTM will be expected to develop new techniques to process large data sets, apply a causal lens to the framework, address ambiguous business problems, and contribute to design of automated systems around the company.You will partner closely with product and program owners, as well as scientists and engineers from other disciplines (e.g. data science, software engineers, data engineering) with a clear path to business impact. You develop innovative and even frighteningly bold plans and ideas to discover new ways to advance our goals. You will be expected to be a thought leader as we chart new courses with our rapidly growing employee populations, and lead the way in experimenting new ideas that have not yet been explored.Key Responsibilities:· Participate in scoping and planning of GTM’s Science roadmap· Uncover drivers, impacts, and key influences on talent outcomes· Build new econometric models to improve existing talent products or those that make the case for new products· Bring a causal lens to questions in human resources employing either experiments or non-experimental approaches· Develop predictive and optimization models for key applications· Navigate a variety of data sources, such as enterprise data, customize surveys, focus groups, and/or external data sources· Ability to distill informal customer requirements into problem definitions, dealing with ambiguity and competing objectives· Work in expert cross-functional teams delivering on demanding projects
US, CA, Virtual Location - California 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.
US, WA, Seattle
The AWS Central Economics team is looking for a PhD economist. The ideal candidate will have experience with time-series forecasting.You will learn about cloud products, including compute, storage, and databases. You will work on analytic projects requested by senior leadership. You will get the opportunity to learn new techniques. You will be a part of a team with many experienced economists.
US, VA, Arlington
Amazon’s Talent Assessment team designs and implements groundbreaking hiring solutions for one of the world’s fastest growing companies. We work in a fast-paced, global environment where we must solve complex problems (ranging from research-based to technical) and deliver solutions that have impact.We are seeking personnel selection researchers with a strong foundation in the development of pre-hire selection assessments, traditional and alternative legally defensible assessment validation approaches, research methodology, and data analysis. We are looking for equal parts researchers and consultant/thought leaders who are highly adaptable and continual learners who thrive in a fast paced environment.You will work closely with global teams to design and experiment new hiring solutions that predict success for highly complex roles (technical and non-technical) that have great impact on Amazon globally.What you’ll do:· Lead the tactical development and execution of large scale, highly visible personnel selection research projects· Develop and iterate on experimental research studies to optimize qualitative and quantitative hiring strategies· · Develop and innovate on new pre-hire test assessment design, validation, and implementation· · Partner with internal and external technology teams· Influence executive project sponsors and multiple business and development teams across the company· Drive effective teamwork, communication, and collaboration across multiple stakeholder groups
US, VA, Arlington
Amazon’s Talent Assessment team designs, implements, and optimizes hiring systems for one of the world’s fastest growing companies. We work in a data-focused, global environment solving complex problems with deep thought, large-sample research, and advanced quantitative methods to deliver practical solutions that make all aspects of hiring more fair, accurate, and efficient.We're looking for a curious data scientist interested in working on a multi-disciplinary team of applied scientists, psychologists, data engineers, business analysts, and program managers. In this role, you will apply your modeling skills to bust myths, create insights, and produce recommendations to help Amazon evaluate millions of potential new hires per year. You'll be involved in all phases of research and experiment design and analysis, including defining research questions, designing experiments, identifying data requirements, conducting statistical or machine learning-based modeling, and communicating insights and recommendations. You'll also be expected to continuously learn new systems, tools, and industry best practices to analyze big data and enhance our analytics.