Fitzgerald keynote.png
Amazon senior applied scientist Jack FitzGerald, delivering a keynote talk at the joint Language Intelligence @ Work and SEMANTiCS conference in Vienna, Austria.

Scaling multilingual virtual assistants to 1,000 languages

Self-supervised training, distributed training, and knowledge distillation have delivered remarkable results, but they’re just the tip of the iceberg.

Yesterday at the joint Language Intelligence @ Work and SEMANTiCS conference in Vienna, Austria, Amazon senior applied scientist Jack FitzGerald delivered a keynote talk on multilingual virtual assistants and the path toward a massively multilingual future. This is an edited version of his talk.

The evolution of human-computer interaction paradigms

In the past 50 years, computing technology has progressed from text-based terminal inputs, to graphical user interfaces, to predominantly web-based applications, through the mobile era, and finally into the era of a voice user interface and ambient computing.

Interface timeline.png
A brief history of computing interfaces.

Each of these paradigms has its own challenges with respect to multilingualism, whether it was the migration from ASCII to Unicode or proper character rendering on a website. However, I would argue that a voice AI system is the most difficult paradigm yet with respect to massive multilingualism.

The first reason is that the input space for voice interface commands is unbounded: the user can phrase each command in hundreds of different ways, all of which are valid. Another reason is that even within a single language, there can be many different dialects and accents.

Related content
Amazon Visiting Academic Barbara Poblete helps to build safer, more-diverse online communities — and to aid disaster response.

Most important, the coupling between language and culture is inescapable. Whether it’s the level of formality used, preferred activities, or religious differences, there isn’t a one-size-fits-all solution. Instead, we must adapt the virtual assistant to understand cultural context and say only things that are appropriate for a given locale.

Voice AI systems today

A typical voice AI system includes automatic-speech-recognition models, which convert raw audio into text; natural-language understanding models, which determine the user’s intent and recognize named entities; a central service for arbitration and dialogue management, which routes commands to the proper services or skills; and finally, a text-to-speech model, which issues the output. Additional tasks might include expansion of the underlying knowledge graph and semantic parsing, localization of touch screen content, or local information services.

Alexa overview.png
An overview of Alexa’s design.

Let’s look at some of the operational considerations for supporting multiple languages in such models. One is the training data: they must be topically exhaustive, meaning that they cover the full spectrum of possible user utterances, and they must be culturally exhaustive — for instance, covering all of the holidays a user might celebrate. They must also remain up-to-date, and it’s not always easy to add something new to the model without regression on existing functionalities.

A second consideration is in-house testing. Though in many cases one can get away with synthetic or otherwise artificial data for model training, for testing it’s important to have realistic utterances. Those typically need to come from humans, and collecting them can be a major expense. It’s also useful to perform live, interactive testing, which requires people who can speak and understand each language that the system supports.

Related content
New approach corrects for cases when average improvements are accompanied by specific regressions.

Finally, it’s important to have the ability to support users and process their feedback. In most cases, this again requires staff who understand each of the supported languages.

Ultimately, human-based processes are not very scalable if our goal is to support thousands of languages. Instead, we must turn to technology to the greatest extent possible.

Multilingual modeling today

One of the leading reasons for the current success of multilingual text models is self-supervision.

In traditional supervised learning, a model would be trained from scratch on the desired task. If we wanted a model that would classify the sentiment of a product review, for example, we would manually annotate a bunch of product reviews, and we would use that dataset to train the model.

Today, however, we make use of transfer learning, in which text models are pretrained on terabytes of text data that don’t require manual annotation. Instead, the training procedure leverages the structure inherent to the text itself.

Self-supervision signals.png
Self-supervised-training objectives.

We’ll call this self-supervised pretraining With the masked-language-modeling training objective, for instance, the model is fed the input “for [MASK] out loud!”, and it must predict that “[MASK]” should be filled with the word “crying”. Other objectives, such as causal language modeling, span filling, deshuffling, and denoising can also be used.

Because the datasets required for self-supervised pretraining are unlabeled and monolingual, we can leverage troves of data, such as Common Crawl web scrapes, every Wikipedia page in existence, thousands of books and news articles, and more. Couple these large datasets with highly parallelizable architectures such as transformers, which can be trained on over a thousand GPUs with near linear scaling, and we can build models with tens or hundreds of billions of dense parameters. Such has been the focus for many people in the field for the past few years, including the Alexa Teacher Model team.

One incredible consequence of the transfer learning paradigm is called zero-shot learning. In the context of multilingual modeling, it works like this: the modeler begins by pretraining the model on some set of languages, using self-supervision. As an example, suppose that the modeler trains a model on English, French, and Japanese using every Wikipedia article in those three languages.

Related content
New end-to-end approach to zero-shot video classification dramatically outperforms predecessors.

The next step is to adapt the model to a particular task using labeled data. Suppose that the modeler has a labeled dataset for intent classification, but only in English. The modeler can go ahead and fine-tune the model on the English data, then run it on the remaining languages.

Despite the fact that the model was never trained to do intent classification with French or Japanese data, it can still classify intents in those languages, by leveraging what it learned about those languages during pretraining. Given that the acquisition of labeled data is often a bottleneck, this property of language models is highly valuable for language expansion. Of course, zero-shot learning is just the extreme end of a continuum: transfer learning helps even out performance when the labeled data in different languages is imbalanced.

Zero-shot multilingual.png
Zero-shot learning for multilingual adaptation.

The next step up the data efficiency ladder is performing tasks without any additional training or fine tuning, using only a couple of labeled records or none at all. This is possible through “in-context learning,” which was popularized in the GPT-3 paper.

To perform in-context learning, simply take a pretrained model and feed it the appropriate prompts. Think of a prompt is a hint to the model about the task it should perform. Suppose that we want the model to summarize a passage. We might prefix the passage with the word “Passage” and a colon and follow it with the word “Summary” and a colon. The model would then generate a summary of the passage.

Related content
In the past few years, advances in artificial intelligence have captured our imaginations and led to the widespread use of voice services on our phones and in our homes.

This is the zero-shot in-context learning case, meaning that no fine-tuning is performed, and no labeled data are needed. To improve task performance, we can feed a few examples to the model before asking it to perform the task. Though this does require some labeled data, the amount is small, usually in the tens of examples only.

Our Alexa Teacher Model team recently trained and tested a 20-billion-parameter sequence-to-sequence model that was multilingual and showed nice performance for in-context learning. For example, we showed state-of-the-art performance on machine translation with in-context learning. The model can achieve competitive BLEU scores even for some low-resource languages, which is incredible given that no parallel data was used during pretraining, and no labeled data besides a single example was used at any step in the process.

We were particularly proud of the relatively small size of this model, which could compete with much larger models because it was trained on more data. (The Chinchilla model from OpenAI showed a similar result.) Though a large model trained on a smaller dataset and a smaller model trained on a larger dataset may use the same total compute at training time, the smaller model will require less compute and memory during inference, which is a key factor in real applications.

Given that models demonstrate multilingual understanding even without labeled data or parallel data, you may be wondering what’s happening inside of the model. Since the days of word2vec and earlier, we’ve represented characters, words, sentences, documents, and other inputs as vectors of floats, also known as embeddings, hidden states, and representations. Concepts cluster in certain areas of the representational space.

Related content
Training a product discovery system on many languages at once improves performance in all of them.

As humans, we can think only in three dimensions, whereas these representations are high-dimensional, but you can visualize this clustering in two or three dimensions as a reductive approximation. All the languages the model supports would cluster the concept of sitting in a chair in one region of the representational space; the concept of the ocean would inhabit a different cluster; and so forth.

Indeed, Pires et al. have shown that synonymous words across languages cluster together in the mBERT model. When examining 5,000 sentence pairs from the WMT16 dataset, they found that, given a sentence and its embedding in one language, the correct translation from another language is the closest embedding to the source embedding up to 75% of the time.

This manner of clustering can also be manipulated by changing the objective function. In their work on speech-to-text-modeling, Adams et al., from Johns Hopkins, were seeing undesirable clustering by language, rather than by phonemes, in the representational space. They were able to correct by adding training objectives around phoneme prediction and language identification.

The Alexa Teacher Model distillation pipeline

Once we have multilingual models, how do we adapt them to a real system? At the recent KDD conference, we presented a paper describing the Alexa Teacher Model pipeline, consisting of the following steps.

First, a multilingual model with billions of parameters is trained on up to a trillion tokens taken from Common Crawl web scrapes, Wikipedia articles, and more. Second, the models are further trained on in-domain, unlabeled data from a real system. Third, the model is distilled into smaller sizes that can be used in production. The final models can then be fine-tuned using labeled data and deployed.

ATM pipeline.png
The Alexa Teacher Model (AlexaTM) pipeline. The Alexa Teacher Model is trained on a large set of GPUs (left), then distilled into smaller variants (center), whose size depends on their uses. The end user adapts a distilled model to its particular use by fine-tuning it on in-domain data (right).

In tests, we found that our model was more accurate than a publicly available pretrained model fine-tuned on labeled data, and it significantly reduced customer dissatisfaction relative to a model trained by a smaller teacher model (85 million parameters, say, instead of billions). In short, we’ve verified that we can leverage the additional learning capacity of large, multilingual models for production systems requiring low latency and low memory consumption.

Scaling to 1,000 languages

I mentioned the fascinating ability of language models to learn joint representations of multiple languages without labeled or parallel data. This ability is crucial for us to scale to many languages. However, as we scale, we need test data that we can trust so that we can evaluate our progress.

Related content
MASSIVE dataset and Massively Multilingual NLU (MMNLU-22) competition and workshop will help researchers scale natural-language-understanding technology to every language on Earth.

Toward this end, my team at Amazon recently released a new benchmark for multilingual natural-language understanding called MASSIVE, which is composed of one million labeled records spanning 51 languages, 18 domains, 60 intents, and 55 slots. All of the data were created by native speakers of the languages. We also released a GitHub repository with code that can be used as a baseline for creating multilingual NLU models, as well as leaderboards on eval.ai.

Now, you may retort that 51 languages is still a long ways from 1,000 languages. This is true, but we purposefully chose our languages in order to maximize typological diversity while staying within our budget. Our languages span 29 language genera, 14 language families, and 21 distinct scripts or alphabets. The diversity of the chosen languages allows a modeler to test technology that should scale to many more languages within each represented genus, family, and script.

That said, we certainly have some major gaps in language coverage, including across native North and South American languages, African languages, and Australian languages. Yet we are optimistic that our fellow researchers across the field will continue to produce new labeled benchmark datasets for the world’s thousands of low-resource languages.

Massive languages.cropped.png
The 51 languages of MASSIVE, including scripts and genera.

Another difficulty with our current modeling approaches is that they rely on data sources such as web scrapes, encyclopedic articles, and news articles, which are highly skewed toward a small set of languages. Wang, Ruder, and Neubig recently presented some fascinating work leveraging bilingual lexicons — corpora consisting of word-level translations — to improve language model performance for low-resource languages. Lexicons cover a far greater portion of the world’s languages than our typical data sources for language modeling, making this an exciting approach.

Related content
Self-learning system uses customers’ rephrased requests as implicit error signals.

Researchers, missionaries, and businesspeople have been created fundamental linguistic resources for decades, from Bible translations to the Unimorph corpus. The Unimorph datasets are used for the SIGMORPHON shared task, in which a model must predict the correct formulation of word given that word’s root and certain morphological transformations, such as part of speech, tense, and person. We must find more ways to leverage such resources when creating massively multilingual voice AI systems.

As a final technique for scaling to many more languages, we can consider what we in Alexa call “self-learning.” Some of my Alexa colleagues published a paper showing that we can mine past utterances to improve overall system performance. For example, if a user rephrases a request as part of a multiturn interaction, as shown on the left in the figure below, or if different users provide variations for the same desired goal, as shown on the right, then we can make soft assumptions that the different formulations are synonymous.

All of these cases can be statistically aggregated to form new training sets to update the system, without the need to manually annotate utterances. In a multilingual system, such technology is particularly valuable after the initial launch of a language, both to improve performance generally and to adapt to changes in the lexicon.

Self-learning.png
Alexa’s self-learning mechanism.

The road ahead

I hope that you share my wonder at the current state of the art — the scale of language-model training, the magic of zero-shot learning, and the distillation of knowledge into compact models that can run in latency-sensitive systems. All of this is incredible, but we’ve only scratched the surface of supporting the world’s 7,000 languages.

To move into the next era of massive multilingualism, we must build new and increasingly powerful models that can take advantage of low-cost data, particularly unlabeled monolingual data. We must also build models that can leverage existing and upcoming linguistic resources, such as bilingual lexicons and morphological-transformation databases. And finally, we must expand available language resources across more languages and domains, including more unlabeled monolingual corpora, more parallel resources, and more realistic, labeled, task-specific datasets.

Increased multilingualism is a win for all people everywhere. Each language provides a unique perspective on the world in which we live. A rich plurality of perspectives leads to a deeper understanding of our fellow people and of all creation.

Keep building.

Research areas

Related content

US, WA, Seattle
The Amazon Devices and Services organization designs, builds and markets Kindle e-readers, Fire Tablets, Fire TV Streaming Media Players and Echo devices. The Device Economics team is looking for an Economist to join our fast paced, start-up environment to help invent the future of product economics. We solve significant business problems in the devices and retail spaces by understanding customer behavior and developing business decision-making frameworks. You will build econometric and machine learning models for causal inference and prediction, using our world class data systems, and apply economic theory to solve business problems in a fast-moving environment. This involves analyzing Amazon Devices and Services customer behavior, and measuring and predicting the lifetime value of existing and future products. We build scalable systems to ensure that our models have broad applicability and large impact. You will work with Scientists, Economists, Product Managers, and Software Developers to provide meaningful feedback about stakeholder problems to inform business solutions and increase the velocity, quality, and scope behind our recommendations. Key job responsibilities Applies expertise in causal modeling to develop econometric/machine learning models to measure the economic value of devices and the business Reviews models and results for other scientists, mentors junior scientists Generates economic insights for the Devices and Services business and work with stakeholders to run the business for effectively Describes strategic importance of vision inside and outside of team. Identifies business opportunities, defines the problem and how to solve it. Engages with scientists, business leadership outside Devices and Services to understand interplay between different business units We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Seattle, WA, USA
US, WA, Seattle
Amazon Advertising's Publisher Technologies team is looking for an experienced Applied Scientist with proven research experience in control theory, online machine learning, and/or mechanism design to drive innovative algorithms for ad-delivery at scale. Your work will directly shape pacing, yield optimization, and ad-selection for Amazon's publishers and impact experiences for hundreds of millions of users and devices. About the team Amazon Advertising operates at the intersection of eCommerce, streaming, and advertising, offering a rich array of digital advertising solutions with the goal of helping our customers find and discover anything they want to buy. We help advertisers reach customers across Amazon's owned and operated sites (publishers) across the web and on millions of devices such as Amazon.com, Prime Video, FreeVee, Kindles, Fire tablets, Fire TV, Alexa, Mobile, Twitch, and more. Within Ads, Publisher Technologies is building the next generation of ad-serving products to allow our publishers to monetize their on-demand, streaming, and static content across Amazon’s ad network in a few clicks. Publishers interact directly with our technology, through programmatic APIs to optimize billions of impression opportunities per day. About the role Publisher Technologies is looking to build out our Publisher Ad Server Science + Simulation and Experimentation team to drive innovation across ad-server delivery algorithms for budget pacing, ad-selection, and yield optimization. We seek to ensure the highest quality experiences for Amazon's customers by matching them with most relevant ads while ensuring optimal yield for publishers. As a Senior Applied Scientist, you will research, invent, and apply cutting edge designs and methodologies in control theory, online optimization, and machine learning to improve publisher yield and customer experience. You will work closely with our engineering and product team to design and implement algorithms in production. In addition, you will contribute to the end state vision of AI enhanced ad-delivery. You will be a foundational member of the team that builds a world-class, green-field ad-delivery service for Amazon's video, audio, and display advertising. To be successful in this role, you must be customer obsessed, have a deep technical background in both online algorithms and distributed systems, comfort dealing with ambiguity, an eye for detail, and a passion to identify and solve for practical considerations that occur when complex control-loops have to operate autonomously and reliably to make millisecond level decisions at scale. You are a technical leader with track record of building control theoretic and/or machine learning models in production to drive business KPIs such as budget delivery. If you are interested working on challenging and practical problems that impact hundreds of millions of users and devices and span cutting edge areas of optimization and AI while having fun on a rapidly expanding team, come join us! Key job responsibilities * Developing new statistical, causal, machine learning, and simulation techniques and develop solution prototypes to drive innovation * Developing an understanding of key business metrics / KPIs and providing clear, compelling analysis that shapes the direction of our business * Working with technical and non-technical customers to design experiments, simulations, and communicate results * Collaborating with our dedicated software team to create production implementations for large-scale data analysis * Staying up-to-date with and contributing to the state-of-the-art research and methodologies in the area of advertising algorithms * Presenting research results to our internal research community * Leading training and informational sessions on our science and capabilities * Your contributions will be seen and recognized broadly within Amazon, contributing to the Amazon research corpus and patent portfolio. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, WA, Seattle
The Alexa Economics team is looking for a Senior Economics Manager who is able to provide structure around complex business problems, hone those complex problems into specific, scientific questions, and test those questions to generate insights. The candidate will work with various product, analytics, science, and engineering teams to develop models and algorithms on large scale data, design pilots and measure their impact, and transform successful prototypes into data products at scale. They will lead teams of researchers to produce robust, objective research results and insights which can be communicated to a broad audience inside and outside of Alexa. Key job responsibilities Ideal candidates will work closely with business partners to develop science that solves the most important business challenges. They will work well in a team setting with individuals from diverse disciplines and backgrounds. They will serve as an ambassador for science for business teams, so that leaders are equipped with the right data and mental model to make important business decisions. Ideal candidates will own the development of scientific models and manage the data analysis, modeling, and experimentation that is necessary for estimating and validating models. They will be customer centric – clearly communicating scientific approaches and findings to business leaders, listening to and incorporate their feedback, and delivering successful scientific solutions. A day in the life - Review new technical approaches to understand Engagement and associated benefits to Alexa. - Partner with Engineering and Product teams to inject econometric insights and models into customer-facing products. - Help business teams understand the key causal inputs that drive business outcome objectives. About the team The Alexa Engagement and Economics and Team uses data, analytics, economics, statistics, and machine learning to measure, report, and track business outputs and growth. We are a team that is obsessed with understanding customer behaviors, and leveraging all aspects from customers behaviors with Alexa and Amazon to develop and deliver solutions that can drive Alexa growth and long-term business success. We use causal inference to identify business optimization and product opportunities. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA | Seattle, WA, USA
US, WA, Bellevue
We are seeking a passionate, talented, and inventive individual to join the Applied AI team and help build industry-leading technologies that customers will love. This team offers a unique opportunity to make a significant impact on the customer experience and contribute to the design, architecture, and implementation of a cutting-edge product. The mission of the Applied AI team is to enable organizations within Worldwide Amazon.com Stores to accelerate the adoption of AI technologies across various parts of our business. We are looking for an Applied Scientist to join our Applied AI team to work on LLM-based solutions. Key job responsibilities You will be responsible for developing and maintaining the systems and tools that enable us to accelerate knowledge operations and work in the intersection of Science and Engineering. You will push the boundaries of ML and Generative AI techniques to scale the inputs for hundreds of billions of dollars of annual revenue for our eCommerce business. If you have a passion for AI technologies, a drive to innovate and a desire to make a meaningful impact, we invite you to become a valued member of our team. A day in the life We are seeking an experienced Scientist who combines superb technical, research, analytical and leadership capabilities with a demonstrated ability to get the right things done quickly and effectively. This person must be comfortable working with a team of top-notch developers and collaborating with our research teams. We’re looking for someone who innovates, and loves solving hard problems. You will be expected to have an established background in building highly scalable systems and system design, excellent project management skills, great communication skills, and a motivation to achieve results in a fast-paced environment. You should be somebody who enjoys working on complex problems, is customer-centric, and feels strongly about building good software as well as making that software achieve its operational goals. About the team On our team you will push the boundaries of ML and Generative AI techniques to scale the inputs for hundreds of billions of dollars of annual revenue for our eCommerce business. If you have a passion for AI technologies, a drive to innovate and a desire to make a meaningful impact, we invite you to become a valued member of our team. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA
US, WA, Bellevue
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 Python is necessary, and experience with SQL and UNIX 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, scientists, and product managers. 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 85% of previous cohorts have converted to full time economics employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Bellevue, WA, USA | Seattle, WA, USA
US, WA, Seattle
The ASFS Team is hiring an Intern 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. Knowledge of econometrics and macroeconomics, as well as familiarity with Python, Matlab, or R is necessary. This is a full-time position at 40 hours per week, with compensation being awarded on an hourly basis. You will use internal and external data to estimate macroeconometric models to answer critical business questions, also you will have the opportunity to collaborate with economists and data scientists. Roughly 85% of interns from previous cohorts have converted to full time economics employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA | New York City, NY, USA | Seattle, WA, USA
US, WA, Bellevue
As an Applied Scientist on our Learning and Development team, you will play a critical role in driving the design, development, and delivery of learning programs and initiatives aimed at enhancing leadership and associate development within the organization. You will leverage your expertise in learning science, data analysis, and statistical model design to create impactful learning journey roadmap that align with organizational goals and priorities. Key job responsibilities 1) Research and Analysis: Conduct research on learning and development trends, theories, and best practices related to leadership and associate development. Analyze data to identify learning needs, performance gaps, and opportunities for improvement within the organization. Use data-driven insights to inform the design and implementation of learning interventions. 2) Program Design and Development: Collaborate with cross-functional teams to develop comprehensive learning programs focused on leadership development and associate growth. Design learning experiences using evidence-based instructional strategies, adult learning principles, and innovative technologies. Create engaging and interactive learning materials, including e-learning modules, instructor-led workshops, and multimedia resources. 3) Evaluation and Continuous Improvement: Develop evaluation frameworks to assess the effectiveness and impact of learning programs on leadership development and associate performance. Collect and analyze feedback from participants and stakeholders to identify strengths, areas for improvement, and future learning needs. Iterate on learning interventions based on evaluation results and feedback to continuously improve program outcomes. 4) Thought Leadership and Collaboration: Serve as a subject matter expert on learning science, instructional design, and leadership development within the organization. Collaborate with stakeholders across the company to align learning initiatives with strategic priorities and business objectives. Share knowledge and best practices with colleagues to foster a culture of continuous learning and development. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA | Nashville, TN, USA
US, WA, Seattle
Amazon Web Services (AWS) is building a world-class marketing organization, and we are looking for an experienced Economist to join the central data and science organization for AWS Marketing. This candidate will develop innovative solutions to measure the return on marketing investments. They will work closely with business leaders, scientists, and engineers to translate business and functional requirements into concrete deliverables, including the design, development, testing, and deployment of innovative measurement solutions. They will interact with functional leaders owning events (e.g. re:Invent, summits, webinars), paid media (paid search, paid social, display), AWS-owned channels (email, website, console) as well as lead management organization to drive the development, fine-tuning and adoption of the consistent measurement framework across these diverse initiatives. We seek candidates with an entrepreneurial spirit who want to make a big impact on AWS growth. They will develop strong working relationships and thrive in a collaborative team environment. They will have the creativity, curiosity, and strong judgment to work on high-impact, high-visibility products to improve the experience of AWS leads and customers. Key job responsibilities - Apply your expertise in causal inference and ML to develop systems to measure B2B marketing impact - Develop and execute science products from concept, prototype to production incorporating feedback from customers, scientists and business leaders - Identify new opportunities for leveraging economic insights and models in the marketing space - Write technical white papers and business-facing documents to clearly explain complex technical concepts to audiences with diverse business/scientific backgrounds We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Austin, TX, USA | New York City, NY, USA | Seattle, WA, USA
US, GA, Atlanta
Looking for your next challenge? North America Sort Centers (NASC) are experiencing growth and looking for a skilled, highly motivated Data Scientist to join the NASC Engineering Data, Product and Simulation Team. The Sort Center network is the critical Middle-Mile solution in the Amazon Transportation Services (ATS) group, linking Fulfillment Centers to the Last Mile. The experience of our customers is dependent on our ability to efficiently execute volume flow through the middle-mile network. Key job responsibilities The Senior Data Scientist will design and implement solutions to address complex business questions using simulation. In this role, you will apply advanced analysis techniques and statistical concepts to draw insights from massive datasets, and create intuitive simulations and data visualizations. You can contribute to each layer of a data solution – you work closely with process design engineers, business intelligence engineers and technical product managers to obtain relevant datasets and create simulation models, and review key results with business leaders and stakeholders. Your work exhibits a balance between scientific validity and business practicality. On this team, you will have a large impact on the entire NASC organization, with lots of opportunity to learn and grow within the NASC Engineering team. This role will be the first dedicated simulation expert, so you will have an exceptional opportunity to define and drive vision for simulation best practices on our team. To be successful in this role, you must be able to turn ambiguous business questions into clearly defined problems, develop quantifiable metrics and deliver results that meet high standards of data quality, security, and privacy. About the team NASC Engineering’s Product and Analytics Team’s sole objective is to develop tools for under the roof simulation and optimization, supporting the needs of our internal and external stakeholders (i.e Process Design Engineering, NASC Engineering, ACES, Finance, Safety and Operations). We develop data science tools to evaluate what-if design and operations scenarios for new and existing sort centers to understand their robustness, stability, scalability, and cost-effectiveness. We conceptualize new data science solutions, using optimization and machine learning platforms, to analyze new and existing process, identify and reduce non-value added steps, and increase overall performance and rate. We work by interfacing with various functional teams to test and pilot new hardware/software solutions. We are open to hiring candidates to work out of one of the following locations: Atlanta, GA, USA | Bellevue, WA, USA
US, WA, Bellevue
Amazon’s Middle Mile Planning & Optimization team is looking for an exceptional Sr. Applied Scientist to solve complex optimization problems that ensure we exceed customer delivery promise expectations and minimize overall operational cost while supporting Amazon’s rapid growth globally. We use cutting edge technologies in large-scale optimization, predictive analytics, and generative AI to optimize the flow of packages within our network to efficiently match network capacity with shipment demand. Our services already handle thousands of requests per second, make business decisions impacting billions of dollars a year, and improve the delivery experience for millions of online shoppers. That said, this remains a fast-growing business and our journey has just started. Our mission is to build the most efficient and optimal transportation solution on the planet, using our technology and engineering muscle as our biggest advantage. Key job responsibilities You will work closely with product managers, research scientists, business/operations leaders, and technical leadership to build capabilities that transform our transportation network. This includes analyzing big data, building end-to-end workflows, prototype optimization/simulation models, and launch production capabilities. You will have exposure to senior leadership as you communicate results and provide scientific guidance to the business. Your insights will be a key influencer of our product strategy and roadmap and your experimental research will inform our future investment areas. About the team You will join the Surface Research Science (SRS) team, which is the science partner of the Middle-Mile Planning & Optimization tech organization. SRS is working on a fascinating range of problems, including some of the hardest and largest optimization, simulation, and prediction problems in the industry. Examples are long-term and short-term demand forecasting, capacity planning, driver scheduling, vehicle routing, and equipment rebalancing problems. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA