Alexa enters the “age of self”

More-autonomous machine learning systems will make Alexa more self-aware, self-learning, and self-service.

Alexa launched in 2014, and in the more than six years since, we’ve been making good on our promise to make Alexa smarter every day. In addition to foundational improvements in Alexa’s core AI technologies, such as speech recognition and natural-language-understanding systems, Alexa scientists have developed technologies that continue to delight our customers, such as whispered speech and Alexa’s new live translation service.

Prem Natarajan, Alexa AI vice president of natural understanding, giving a presentation
Prem Natarajan, Alexa AI vice president of natural understanding, at a conference in 2018.

But some of the technologies we’ve begun to introduce, together with others we’re now investigating, are harbingers of a step change in Alexa’s development — and in the field of AI itself. Collectively, these technologies will bring a new level of generalizability and autonomy to both the Alexa voice service and the tools available to Alexa developers, ushering in what I like to think of as a new “age of self” in artificial intelligence, an age in which AI systems such as Alexa become more self-aware and more self-learning, and in which they lend themselves to self-service by experienced developers and even end users.

By self-awareness, I mean the ability to maintain an awareness of ambient state (e.g., time of day, thermostat readings, and recent actions) and to employ commonsense reasoning to make inferences that reflect that awareness and prior/world knowledge. Alexa hunches can already recognize anomalies in customers’ daily routines and suggest corrections — noticing that a light was left on at night and offering to turn it off, for instance. Powered by commonsense reasoning, self-awareness goes further: for instance, if a customer turns on the television five minutes before the kids’ soccer practice is scheduled to end, an AI of the future might infer that the customer needs a reminder about pickup.

Smart home.png
In the "age of self", AIs will be able to infer customers’ implicit intentions from observable temporal patterns, such as interactions with smart-home devices like thermostats, door locks, and lights.

Self-learning is Alexa’s ability to improve and expand its capabilities without human intervention. And like self-awareness, self-learning employs reasoning: for example, does the customer’s response to an action indicate dissatisfaction with that action? Similarly, when a customer issues an unfamiliar command, a truly self-learning Alexa would be able to infer what it might mean — perhaps by searching the web or exploring a knowledge base — and suggest possibilities.

Self-service means, essentially, the democratization of AI. Alexa customers with no programming experience should be able to customize Alexa’s services and even create new Alexa capabilities, and skill developers without machine learning experience should be able to build complex yet robust conversational skills. Colloquially, these are the conversational-AI equivalents of no-code and low-code development environments.

To be clear, the age of self is not yet upon us, and its dawning will require the maturation of technologies still under development, at Amazon and elsewhere. But some of Alexa’s recently launched capabilities herald a lightening in the Eastern sky.

Self-awareness

In 2018, we launched Alexa hunches for the smart home, with Alexa suggesting actions to take in response to anomalous sensor data. By early 2021, the science has advanced adequately for us to launch an opt-in service in which Alexa can take action immediately and automatically. In the meantime, we’ve also been working to expand hunches to Alexa services other than the smart home.

Technologies will bring a new level of generalizability and autonomy to both the Alexa voice service and the tools available to Alexa developers, ushering in what I like to think of as a new 'age of self' in artificial intelligence.
Prem Natarajan

But commonsense reasoning requires something more — the ability to infer customers’ implicit intentions from observable temporal patterns. For instance, what does it mean if the customer turns down the thermostat, turns out the lights, locks the front door, and opens the garage? What if the customer initiates an interaction with a query like “Alexa, what’s playing at Rolling Hills Cine Plaza?”

In 2020, we took steps toward commonsense reasoning with a new Alexa function that can infer a customer’s latent goal— the ultimate aim that lies behind a sequence of requests. When a customer asks for the weather at the beach, for instance, Alexa might use that query, in combination with other contextual information, to infer that the customer may be interested in a trip to the beach. Alexa could then offer the current driving time to the beach.

To retrieve that information, Alexa has to know to map the location of the weather request to the destination variable in the route-planning function. This illustrates another aspect of self-awareness: the ability to track information across contexts.

That ability is at the core of the night-out experience we’ve developed, which engages the customer in a multiturn conversation to plan a complete night out, from buying movie tickets to making restaurant and ride-share reservations. The night-out experience tracks times and locations across skills, revising them on the fly as customers evaluate different options. To build the experience, we leveraged the machinery of Alexa Conversations, a service that enables developers to quickly and easily create dialogue-driven skills, and we drew on our growing body of research on dialogue state tracking.

Slot_tracking.png._CB436837753_.png
Dialogue states at several successive dialogue turns

Self-awareness, however, includes an understanding not only of the conversational context but also of the customer’s physical context. In 2020, we demonstrated natural turn-taking on Alexa-enabled devices with cameras. When multiple speakers are engaging with Alexa, Alexa can use visual cues to distinguish between speech the customers are directing at each other and speech they’re directing at Alexa. In ongoing work, we’re working to expand this functionality to devices without cameras, by relying solely on acoustic and linguistic signals.

Finally, self-awareness also entails the capacity for self-explanation. Today, most machine learning models are black boxes; even their creators have no idea how they’re doing what they do. That uncertainty has turned explainable or interpretable AI into a popular research topic.

Amazon actively publishes on explainable-AI topics. In addition, the Alexa Fund, an Amazon venture capital investment program, invested in fiddler.ai, a startup that uses techniques based on the game-theoretical concept of Shapley values to do explainable AI.

Self-learning

Historically, the AI development cycle has involved collection of data, annotation of that data, and retraining of models on the newly annotated data — all of which add up to a laborious process.

In 2019, we launched Alexa’s self-learning system, which automatically learns to correct errors — both customer errors and errors in Alexa’s language-understanding models — without human involvement. The system relies on implicit signals that a request was improperly handled, as when a customer interrupts a response and rephrases the same request.

Absorbing-Markov-chain models for three different sequences of utterances
Alexa's self-learning system models customer interactions with Alexa as sequences of states; different customer utterances (u0, u1, u2) can correspond to the same state (h0). The final state of a sequence, known as the "absorbing state", indicates the success (checkmark) or failure (X) of a transaction.
Stacy Reilly

Currently, that fully automatic system is correcting 15% of defects. But those are defects that occur across a spectrum of users; only when enough people implicitly identify the same flaw does the system address it. We are working to adapt the same machinery to individual customers’ preferences — so that, for instance, Alexa can learn that when a particular customer asks for the song “Wow”, she means not the Post Malone hit from 2019 but the 1978 Kate Bush song.

Customers today also have the option of explicitly teaching Alexa their preferences. In the fall of 2020, we launched interactive teaching by customers, a capability that enables customers to instruct Alexa how they want certain requests to be handled. For instance, the customer can teach Alexa that the command “reading mode” means lights turned all the way up, while “movie mode” means only twenty percent up.

Self-service

Interactive teaching is also an early example of how Alexa is enabling more self-service. It extends prior Alexa features, like blueprints, which let customers build their own simple skills from preexisting templates, and routines, which let customers chain together sequences of actions under individual commands.

In March 2021, we announced the public release of Alexa Conversations, which allows developers to create dialogue-driven skills by uploading sample dialogues. Alexa Conversations’ sophisticated machine learning models use those dialogues as templates for generating larger corpora of synthetic training data. From that data, Alexa Conversations automatically trains a machine learning model.

Alexa Conversations does, however, require the developer to specify the set of entities that the new model should act upon and an application programming interface for the skill. So while it requires little familiarity with machine learning, it assumes some programming experience. 

ambiguous_slots.gif._CB438712971_.gif
An Alexa feature known as catalogue value suggestions suggests entity names to skill developers on the basis of their "embeddings", or locations in a representational space. If the embeddings of values (such as bird, dog, or cat) for a particular entity type are close enough (dotted circles) to their averages (solid circle and square), the system suggests new entity names; otherwise, it concludes that suggestions would be unproductive.
Animation by Nick Little

We are steadily chipping away at even that requirement, by making development for Alexa easier and more intuitive. As Alexa’s repertory of skills grows, for instance, entities are frequently reused, and we already have systems that can inform developers about entity types that they might not have thought to add to their skills. This is a step toward a self-service model in which developers no longer have to provide exhaustive lists of entities — or, in some cases, any entities at all.

Another technique that makes it easier to build machine learning models is few-shot learning, in which an existing model is generalized to a related task using only a handful of new training examples. This is an active area of research at Alexa: earlier this year, for example, we presented a paper at the Spoken Language Technologies conference that described a new approach to few-shot learning for natural-language-understanding tasks. Compared to its predecessors, our approach reduced the error rate on certain natural-language-understanding tasks by up to 12.4%, when each model was trained on only 10 examples.

These advances, along with the others reported on Amazon Science, demonstrate that the Alexa AI team continues to accelerate its pace of invention. More exciting announcements lie just over the horizon. I’ll be stopping back here every once in a while to update you on Alexa’s journey into the age of self.

Research areas

Related content

IN, TS, Hyderabad
Welcome to the Worldwide Returns & ReCommerce team (WWR&R) at Amazon.com. WWR&R is an agile, innovative organization dedicated to ‘making zero happen’ to benefit our customers, our company, and the environment. Our goal is to achieve the three zeroes: zero cost of returns, zero waste, and zero defects. We do this by developing products and driving truly innovative operational excellence to help customers keep what they buy, recover returned and damaged product value, keep thousands of tons of waste from landfills, and create the best customer returns experience in the world. We have an eye to the future – we create long-term value at Amazon by focusing not just on the bottom line, but on the planet. We are building the most sustainable re-use channel we can by driving multiple aspects of the Circular Economy for Amazon – Returns & ReCommerce. Amazon WWR&R is comprised of business, product, operational, program, software engineering and data teams that manage the life of a returned or damaged product from a customer to the warehouse and on to its next best use. Our work is broad and deep: we train machine learning models to automate routing and find signals to optimize re-use; we invent new channels to give products a second life; we develop highly respected product support to help customers love what they buy; we pilot smarter product evaluations; we work from the customer backward to find ways to make the return experience remarkably delightful and easy; and we do it all while scrutinizing our business with laser focus. You will help create everything from customer-facing and vendor-facing websites to the internal software and tools behind the reverse-logistics process. You can develop scalable, high-availability solutions to solve complex and broad business problems. We are a group that has fun at work while driving incredible customer, business, and environmental impact. We are backed by a strong leadership group dedicated to operational excellence that empowers a reasonable work-life balance. As an established, experienced team, we offer the scope and support needed for substantial career growth. Amazon is earth’s most customer-centric company and through WWR&R, the earth is our customer too. Come join us and innovate with the Amazon Worldwide Returns & ReCommerce team!
GB, MLN, Edinburgh
We’re looking for a Machine Learning Scientist in the Personalization team for our Edinburgh office experienced in generative AI and large models. You will be responsible for developing and disseminating customer-facing personalized recommendation models. This is a hands-on role with global impact working with a team of world-class engineers and scientists across the Edinburgh offices and wider organization. You will lead the design of machine learning models that scale to very large quantities of data, and serve high-scale low-latency recommendations to all customers worldwide. You will embody scientific rigor, designing and executing experiments to demonstrate the technical efficacy and business value of your methods. You will work alongside a science team to delight customers by aiding in recommendations relevancy, and raise the profile of Amazon as a global leader in machine learning and personalization. Successful candidates will have strong technical ability, focus on customers by applying a customer-first approach, excellent teamwork and communication skills, and a motivation to achieve results in a fast-paced environment. Our position offers exceptional opportunities for every candidate to grow their technical and non-technical skills. If you are selected, you have the opportunity to make a difference to our business by designing and building state of the art machine learning systems on big data, leveraging Amazon’s vast computing resources (AWS), working on exciting and challenging projects, and delivering meaningful results to customers world-wide. Key job responsibilities Develop machine learning algorithms for high-scale recommendations problems. Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative analysis and business judgement. Collaborate with software engineers to integrate successful experimental results into large-scale, highly complex Amazon production systems capable of handling 100,000s of transactions per second at low latency. Report results in a manner which is both statistically rigorous and compellingly relevant, exemplifying good scientific practice in a business environment.
US, WA, Seattle
Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! In Prime Video READI, our mission is to automate infrastructure scaling and operational readiness. We are growing a team specialized in time series modeling, forecasting, and release safety. This team will invent and develop algorithms for forecasting multi-dimensional related time series. The team will develop forecasts on key business dimensions with optimization recommendations related to performance and efficiency opportunities across our global software environment. As a founding member of the core team, you will apply your deep coding, modeling and statistical knowledge to concrete problems that have broad cross-organizational, global, and technology impact. Your work will focus on retrieving, cleansing and preparing large scale datasets, 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 complete independence and are often assigned to focus on areas where the business and/or architectural strategy has not yet been defined. You must be equally comfortable digging in to business 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. 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 delivering for our customers, continually earning their trust, and thinking long term. You are empowered to bring new technologies to your solutions. If you crave a sense of ownership, this is the place to be.
US, WA, Seattle
Amazon Advertising operates at the intersection of eCommerce and advertising, and is investing heavily in building a world-class advertising business. We are 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 to improve both shopper and advertiser experience. With a broad mandate to experiment and innovate, we grow at an unprecedented rate with a seemingly endless range of new opportunities. The Ad Response Prediction team in Sponsored Products organization build advanced deep-learning models, large-scale machine-learning pipelines, and real-time serving infra to match shoppers’ intent to relevant ads on all devices, for all contexts and in all marketplaces. Through precise estimation of shoppers’ interaction with ads and their long-term value, we aim to drive optimal ads allocation and pricing, and help to deliver a relevant, engaging and delightful ads experience to Amazon shoppers. As the business and the complexity of various new initiatives we take continues to grow, we are looking for talented Applied Scientists to join the team. Key job responsibilities As a Applied Scientist II, you will: * Conduct hands-on data analysis, build large-scale machine-learning models and pipelines * Work closely with software engineers on detailed requirements, technical designs and implementation of end-to-end solutions in production * Run regular A/B experiments, gather data, perform statistical analysis, and communicate the impact to senior management * Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving * Provide technical leadership, research new machine learning approaches to drive continued scientific innovation * Be a member of the Amazon-wide Machine Learning Community, participating in internal and external MeetUps, Hackathons and Conferences
US, WA, Seattle
Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video team member, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! Key job responsibilities As an Applied Scientist in the Content Understanding Team, you will lead the end-to-end research and deployment of video and multi-modal models applied to a variety of downstream applications. More specifically, you will: - Work backwards from customer problems to research and design scientific approaches for solving them - Work closely with other scientists, engineers and product managers to expand the depth of our product insights with data, create a variety of experiments to determine the high impact projects to include in planning roadmaps - Stay up-to-date with advancements and the latest modeling techniques in the field - Publish your research findings in top conferences and journals About the team Our Prime Video Content Understanding team builds holistic media representations (e.g. descriptions of scenes, semantic embeddings) and apply them to new customer experiences supply chain problems. Our technology spans the entire Prime Video catalogue globally, and we enable instant recaps, skip intro timing, ad placement, search, and content moderation.
IN, HR, Gurugram
We're on a journey to build something new a green field project! Come join our team and build new discovery and shopping products that connect customers with their vehicle of choice. We're looking for a talented Senior Applied Scientist to join our team of product managers, designers, and engineers to design, and build innovative automotive-shopping experiences for our customers. This is a great opportunity for an experienced engineer to design and implement the technology for a new Amazon business. We are looking for a Applied Scientist to design, implement and deliver end-to-end solutions. We are seeking passionate, hands-on, experienced and seasoned Senior Applied Scientist who will be deep in code and algorithms; who are technically strong in building scalable computer vision machine learning systems across item understanding, pose estimation, class imbalanced classifiers, identification and segmentation.. You will drive ideas to products using paradigms such as deep learning, semi supervised learning and dynamic learning. As a Senior Applied Scientist, you will also help lead and mentor our team of applied scientists and engineers. You will take on complex customer problems, distill customer requirements, and then deliver solutions that either leverage existing academic and industrial research or utilize your own out-of-the-box but pragmatic thinking. In addition to coming up with novel solutions and prototypes, you will directly contribute to implementation while you lead. A successful candidate has excellent technical depth, scientific vision, project management skills, great communication skills, and a drive to achieve results in a unified team environment. You should enjoy the process of solving real-world problems that, quite frankly, haven’t been solved at scale anywhere before. Along the way, we guarantee you’ll get opportunities to be a bold disruptor, prolific innovator, and a reputed problem solver—someone who truly enables AI and robotics to significantly impact the lives of millions of consumers. Key job responsibilities Architect, design, and implement Machine Learning models for vision systems on robotic platforms Optimize, deploy, and support at scale ML models on the edge. Influence the team's strategy and contribute to long-term vision and roadmap. Work with stakeholders across , science, and operations teams to iterate on design and implementation. Maintain high standards by participating in reviews, designing for fault tolerance and operational excellence, and creating mechanisms for continuous improvement. Prototype and test concepts or features, both through simulation and emulators and with live robotic equipment Work directly with customers and partners to test prototypes and incorporate feedback Mentor other engineer team members. A day in the life - 6+ years of building machine learning models for retail application experience - PhD, or Master's degree and 6+ years of applied research experience - Experience programming in Java, C++, Python or related language - Experience with neural deep learning methods and machine learning - Demonstrated expertise in computer vision and machine learning techniques.
US, WA, Seattle
Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video team member, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! Key job responsibilities As an Applied Scientist in the Content Understanding Team, you will lead the end-to-end research and deployment of video and multi-modal models applied to a variety of downstream applications. More specifically, you will: - Work backwards from customer problems to research and design scientific approaches for solving them - Work closely with other scientists, engineers and product managers to expand the depth of our product insights with data, create a variety of experiments to determine the high impact projects to include in planning roadmaps - Stay up-to-date with advancements and the latest modeling techniques in the field - Publish your research findings in top conferences and journals About the team Our Prime Video Content Understanding team builds holistic media representations (e.g. descriptions of scenes, semantic embeddings) and apply them to new customer experiences supply chain problems. Our technology spans the entire Prime Video catalogue globally, and we enable instant recaps, skip intro timing, ad placement, search, and content moderation.
US, WA, Seattle
Do you want to re-invent how millions of people consume video content on their TVs, Tablets and Alexa? We are building a free to watch streaming service called Fire TV Channels (https://techcrunch.com/2023/08/21/amazon-launches-fire-tv-channels-app-400-fast-channels/). Our goal is to provide customers with a delightful and personalized experience for consuming content across News, Sports, Cooking, Gaming, Entertainment, Lifestyle and more. You will work closely with engineering and product stakeholders to realize our ambitious product vision. You will get to work with Generative AI and other state of the art technologies to help build personalization and recommendation solutions from the ground up. You will be in the driver's seat to present customers with content they will love. Using Amazon’s large-scale computing resources, you will ask research questions about customer behavior, build state-of-the-art models to generate recommendations and run these models to enhance the customer experience. You will participate in the Amazon ML community and mentor Applied Scientists and Software Engineers with a strong interest in and knowledge of ML. Your work will directly benefit customers and you will measure the impact using scientific tools.
US, MA, Boston
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background, to build industry-leading technology with Large Language Models (LLMs) and multi-modal systems. You will support projects that work on technologies including multi-modal model alignment, moderation systems and evaluation. Key job responsibilities As an Applied Scientist with the AGI team, you will support the development of novel algorithms and modeling techniques, to advance the state of the art with LLMs. 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 generative artificial intelligence (GenAI). You are also expected to publish in top tier conferences. About the team The AGI team has a mission to push the envelope in LLMs and multimodal systems. Specifically, we focus on model alignment with an aim to maintain safety while not denting utility, in order to provide the best-possible experience for our customers.
IN, HR, Gurugram
Our customers have immense faith in our ability to deliver packages timely and as expected. A well planned network seamlessly scales to handle millions of package movements a day. It has monitoring mechanisms that detect failures before they even happen (such as predicting network congestion, operations breakdown), and perform proactive corrective actions. When failures do happen, it has inbuilt redundancies to mitigate impact (such as determine other routes or service providers that can handle the extra load), and avoids relying on single points of failure (service provider, node, or arc). Finally, it is cost optimal, so that customers can be passed the benefit from an efficiently set up network. Amazon Shipping is hiring Applied Scientists to help improve our ability to plan and execute package movements. As an Applied Scientist in Amazon Shipping, you will work on multiple challenging machine learning problems spread across a wide spectrum of business problems. You will build ML models to help our transportation cost auditing platforms effectively audit off-manifest (discrepancies between planned and actual shipping cost). You will build models to improve the quality of financial and planning data by accurately predicting ship cost at a package level. Your models will help forecast the packages required to be pick from shipper warehouses to reduce First Mile shipping cost. Using signals from within the transportation network (such as network load, and velocity of movements derived from package scan events) and outside (such as weather signals), you will build models that predict delivery delay for every package. These models will help improve buyer experience by triggering early corrective actions, and generating proactive customer notifications. Your role will require you to demonstrate Think Big and Invent and Simplify, by refining and translating Transportation domain-related business problems into one or more Machine Learning problems. You will use techniques from a wide array of machine learning paradigms, such as supervised, unsupervised, semi-supervised and reinforcement learning. Your model choices will include, but not be limited to, linear/logistic models, tree based models, deep learning models, ensemble models, and Q-learning models. You will use techniques such as LIME and SHAP to make your models interpretable for your customers. You will employ a family of reusable modelling solutions to ensure that your ML solution scales across multiple regions (such as North America, Europe, Asia) and package movement types (such as small parcel movements and truck movements). You will partner with Applied Scientists and Research Scientists from other teams in US and India working on related business domains. Your models are expected to be of production quality, and will be directly used in production services. You will work as part of a diverse data science and engineering team comprising of other Applied Scientists, Software Development Engineers and Business Intelligence Engineers. You will participate in the Amazon ML community by authoring scientific papers and submitting them to Machine Learning conferences. You will mentor Applied Scientists and Software Development Engineers having a strong interest in ML. You will also be called upon to provide ML consultation outside your team for other problem statements. If you are excited by this charter, come join us!