Amazon at ICLR: Graphs, time series, and more

Other paper topics include natural-language processing, dataset optimization, and the limits of existing machine learning techniques.

Time series forecasting and graph representations of data are both major topics of research at Amazon: time series forecasting is crucial to both supply chain optimization and product recommendation, and graph representations help make sense of the large datasets that are common at Amazon’s scale, such as the Amazon product catalogue.

Related content
Amazon’s Stefano Soatto on how learning representations came to dominate machine learning.

So it’s no surprise that both topics are well represented among the Amazon papers at the 2022 International Conference on Learning Representations (ICLR), which takes place this week. Another paper also touches on one of Amazon’s core scientific interests, natural-language processing, or computation involving free-form text inputs.

The remaining Amazon papers discuss more general machine learning techniques, such as data augmentation, or automatically selecting or generating training examples that can improve the performance of machine learning models. Another paper looks at dataset optimization more generally, proposing a technique that could be used to evaluate individual examples for inclusion in a dataset or exclusion from it. And two papers from Amazon Web Services’ Causal-Representation Learning team, which includes Amazon vice president and distinguished scientist Bernhard Schölkopf, examine the limitations of existing approaches to machine learning.

Graphs

Graphs represent data as nodes, usually depicted as circles, and edges, usually depicted as line segments connecting nodes. Graph-structured data can make machine learning more efficient, because the graph explicitly encodes relationships that a machine learning model would otherwise have to infer from data correlations.

Graph neural networks (GNNs) are a powerful tool for working with graph-structured data. Like most neural networks, GNNs produce embeddings, or fixed-length vector representations of input data, that are useful for particular computational tasks. In the case of GNNs, the embeddings capture information about both the object associated with a given node and the structure of the graph.

In real-world applications — say, a graph indicating which products tend to be purchased together — some nodes may not be connected to any others, and some connections may be spurious inferences from sparse data. In “Cold Brew: Distilling graph node representations with incomplete or missing neighborhoods”, Amazon scientists present a method for handling nodes whose edge data is absent or erroneous.

Cold Brew data distribution 16x9.png
Cold Brew addresses the real-world problem in which graph representations of data feature potentially spurious connections (tail nodes) or absent connections (cold start). Figure from "Cold Brew: Distilling graph node representations with incomplete or missing neighborhoods".

In a variation on knowledge distillation, they use a conventional GNN, which requires that each input node be connected to the rest of the graph, to train a teacher network that can produce embeddings for connected nodes. Then they train a standard multilayer perceptron — a student network — to mimic the teacher’s outputs. Unlike a conventional GNN, the student network doesn’t explicitly use structural data to produce embeddings, so it can also handle unconnected nodes. The method demonstrates significant improvements over existing methods of inferring graph structure on several benchmark datasets.

Across disciplines, AI research has recently seen a surge in the popularity of self-supervised learning, in which a machine learning model is first trained on a “proxy task”, which is related to but not identical to the target task, using unlabeled or automatically labeled data. Then the model is fine-tuned on labeled data for the target task.

With GNNs, the proxy tasks generally teach the network only how to represent node data. But in “Node feature extraction by self-supervised multi-scale neighborhood prediction”, Amazon researchers and their colleagues at the University of Illinois and UCLA present a proxy task that teaches the GNN how to represent information about graph structure as well. Their approach is highly scalable, working with graphs with hundreds of millions of nodes, and in experiments, they show that it improves GNN performance on three benchmark datasets, by almost 30% on one of them.

XRT for graph neighborhoods.png
XR-Transformer creates a hierarchical tree that sorts data into finer- and finer-grained clusters. In the context of graph neural networks, the clusters represent graph neighborhoods. Figure from "Node feature extraction by self-supervised multi-scale neighborhood prediction".

The approach, which builds on Amazon’s XR-Transformer model and is known as GIANT-XRT, has already been widely adopted and is used by the leading teams in several of the public Open Graph Benchmark competitions hosted by Stanford University (leaderboard 1 | leaderboard 2 | leaderboard 3).

Domain graph.png
Where traditional domain adaptation (left) treats all target domains the same, a new method (right) uses graphs to represent relationships between source and target domains. For instance, weather patterns in adjacent U.S. states tend to be more similar than the weather patterns in states distant from each other. Figure from “Graph-relational domain adaptation”.

A third paper, “Graph-relational domain adaptation”, applies graphs to the problem of domain adaptation, or optimizing a machine learning model to work on data with a different distribution than the data it was trained on. Conventional domain adaptation techniques treat all target domains the same, but the Amazon researchers and their colleagues at Rutgers and MIT instead use graphs to represent relationships among all source and target domains. For instance, weather patterns in adjacent U.S. states tend to be more similar than the weather patterns in states distant from each other. In experiments, the researchers show that their method improves on existing domain adaptation methods on both synthetic and real-world datasets.

Time series

Time series forecasting is essential to demand prediction, which Amazon uses to manage inventory, and it’s also useful for recommendation, which can be interpreted as continuing a sequence of product (say, music or movie) selections.

In “Bridging recommendation and marketing via recurrent intensity modeling”, Amazon scientists adapt existing mechanisms for making personal recommendations on the basis of time series data (purchase histories) to the problem of identifying the target audience for a new product.

UserRec 16x9.png
Product recommendation can be interpreted as a time-series-forecasting problem, in which a product is recommended according to its likelihood of continuing a sequence of purchases. Figure from "Bridging recommendation and marketing via recurrent intensity modeling".

Where methods for identifying a product’s potential customers tend to treat customers as atemporal collections of purchase decisions, the Amazon researchers instead frame the problem as optimizing both the product’s relevance to the customer and the customer’s activity level, or likelihood of buying any product in a given time span. In experiments, this improved the accuracy of a prediction model on several datasets.

One obstacle to the development of machine learning models that base predictions on time series data is the availability of training examples. In “PSA-GAN: Progressive self attention GANs for synthetic time series”, Amazon researchers propose a method for using generative adversarial networks (GANs) to artificially produce time series training data.

Related content
In 2017, when the journal IEEE Internet Computing was celebrating its 20th anniversary, its editorial board decided to identify the single paper from its publication history that had best withstood the “test of time”. The honor went to a 2003 paper called “Amazon.com Recommendations: Item-to-Item Collaborative Filtering”, by then Amazon researchers Greg Linden, Brent Smith, and Jeremy York.

GANs pit generators, which produce synthetic data, against discriminators, which try to distinguish synthetic data from real. The two are trained together, each improving the performance of the other.

The Amazon researchers show how to synthesize plausible time series data by progressively growing — or adding network layers to — both the generator and the discriminator. This enables the generator to first learn general characteristics that the time series as a whole should have, then learn how to produce series that exhibit those characteristics.

Data augmentation

In addition to the paper on synthetic time series, one of Amazon’s other papers at ICLR, “Deep AutoAugment”, also focuses on data augmentation.

It’s become standard practice to augment the datasets used to train machine learning models by subjecting real data to sequences of transformations. For instance, a training image for a computer vision task might be flipped, stretched, rotated or cropped, or its color or contrast might be modified. Typically, the first few transformations are selected automatically, based on experiments in which a model is trained and retrained, and then domain experts add a few additional transformations to try to make the modified data look like real data.

Related content
New method enables users to specify properties such as subject age, light direction, and pose in images produced by generative adversarial networks.

In “Deep AutoAugment”, former Amazon senior applied scientist Zhi Zhang and colleagues at Michigan State University propose a method for fully automating the construction of a data augmentation pipeline. The goal is to continuously add transformations that steer the feature distribution of the synthetic data toward that of the real data. To do that, the researchers use gradient matching, or identifying training data whose sequential updates to the model parameters look like those of the real data. In tests, this approach improved on 10 other data augmentation techniques across four sets of real data.

Natural-language processing

Many natural-language-processing tasks involve pairwise comparison of sentences. Cross-encoders, which map pairs of sentences against each other, yield the most accurate comparison, but they’re computationally intensive, as they need to compute new mappings for every sentence pair. Moreover, converting a pretrained language model into a cross-encoder requires fine-tuning it on labeled data, which is resource intensive to acquire.

Bi-encoders, on the other hand, embed sentences in a common representational space and measure the distances between them. This is efficient but less accurate.

In “Trans-encoder: Unsupervised sentence-pair modelling through self- and mutual-distillations”, Amazon researchers, together with a former intern, propose a model that is trained in an entirely unsupervised way — that is, without unlabeled examples — and captures advantages of both approaches.

Trans-encoder.png
The trans-encoder training process, in which a bi-encoder trained in an unsupervised fashion creates training targets for a cross-encoder, which in turn outputs training targets for the bi-encoder.

The researchers begin with a pretrained language model, fine-tune it in an unsupervised manner using bi-encoding, then use the fine-tuned model to generate training targets for cross-encoding. They then use the outputs of the cross-encoding model to fine-tune the bi-encoder, iterating back and forth between the two approaches until training converges. In experiments, their model outperformed multiple state-of-the-art unsupervised sentence encoders on several benchmark tasks, with improvements of up to 5% over the best-performing prior models.

Dataset optimization

Weeding errors out of a dataset, selecting new training examples to augment a dataset, and determining how to weight the data in a dataset to better match a target distribution are all examples of dataset optimization. Assessing individual training examples’ contribution to the accuracy of a model, however, is difficult: retraining the model on a dataset with and without every single example is hardly practical.

In “DIVA: Dataset derivative of a learning task”, Amazon researchers show how to compute the dataset derivative: a function that can be used to assess a given training example’s utility relative to a particular neural-network model. During training, the model learns not only the weights of network parameters but also weights for individual training examples. The researchers show that, using a linearization technique, they can derive a closed-form equation for the dataset derivative, allowing them to assess the utility of a given training example without retraining the network.

DIVA weighting.png
Training examples that DIVA assigns high weights (left) and low (right) for the task of classifying aircraft. Figure from "DIVA: Dataset derivative of a learning task".

Limitations

“Machine learning ultimately is based on statistical dependencies,” Bernhard Schölkopf recently told Amazon Science. “Oftentimes, it's enough if we work at the surface and just learn from these dependencies. But it turns out that it's only enough as long as we're in this setting where nothing changes.”

The two ICLR papers from the Causal Representation Learning team explore contexts in which learning statistical dependencies is not enough. “Visual representation learning does not generalize strongly within the same domain” describes experiments with image datasets in which each image is defined by specific values of a set of variables — say, different shapes of different sizes and colors, or faces that are either smiling or not and differ in hair color or age.

The researchers test 17 machine learning models and show that, if certain combinations of variables or specific variable values are held out of the training data, all 17 have trouble recognizing them in the test data. For instance, a model trained to recognize small hearts and large squares has trouble recognizing large hearts and small squares. This suggests that we need revised training techniques or model designs to ensure that machine learning systems are really learning what they’re supposed to.

Visual representation learning.png
An illustration of the four methods of separating training data (black dots) and test data (red dots) in "Visual representation learning does not generalize strongly within the same domain".

Similarly, in “You mostly walk alone: Analyzing feature attribution in trajectory prediction”, members of the team consider the problem of predicting the trajectories of moving objects as they interact with other objects, an essential capacity for self-driving cars and other AI systems. For instance, if a person is walking down the street, and a ball bounces into her path, it could be useful to know that the person might deviate from her trajectory to retrieve the ball.

Adapting the game-theoretical concept of Shapley values, which enable the isolation of different variables’ contributions to an outcome, the researchers examine the best-performing recent models for predicting trajectories in interactive contexts and show that, for the most part, their predictions are based on past trajectories; they pay little attention to the influence of interactions.

Trajectory interactions.png
A new method enables the comparison of different trajectory prediction models according to the extent to which they use social interactions for making predictions (left: none; middle: weak; right: strong). The target agent, whose future trajectory is to be predicted, is shown in red, and modeled interactions are represented by arrows whose width indicates interaction strength. From "You mostly walk alone: Analyzing feature attribution in trajectory prediction".

The one exception is a models trained on a dataset of basketball video, where all the players’ movements are constantly coordinated. There, existing models do indeed learn to recognize the influence of interaction. This suggests that careful curation of training data could enable existing models to account for interactions when predicting trajectories.

Research areas

Related content

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, 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!
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 Senior Applied Scientist with a strong deep learning background, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As a Senior Applied Scientist with the AGI team, you will work with talented peers to lead 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). About the team The AGI team has a mission to push the envelope in LLMs and multimodal systems, in order to provide the best-possible experience for our customers.
IN, KA, Bengaluru
The Amazon Alexa AI team in India is seeking a talented, self-driven Applied Scientist to work on prototyping, optimizing, and deploying ML algorithms within the realm of Generative AI. Key responsibilities include: - Research, experiment and build Proof Of Concepts advancing the state of the art in AI & ML for GenAI. - Collaborate with cross-functional teams to architect and execute technically rigorous AI projects. - Thrive in dynamic environments, adapting quickly to evolving technical requirements and deadlines. - Engage in effective technical communication (written & spoken) with coordination across teams. - Conduct thorough documentation of algorithms, methodologies, and findings for transparency and reproducibility. - Publish research papers in internal and external venues of repute - Support on-call activities for critical issues Basic Qualifications: - Master’s or PhD in computer science, statistics or a related field or relevant science experience (publications/scientific prototypes) in lieu of Masters - Experience in deep learning, machine learning, and data science. - Proficiency in coding and software development, with a strong focus on machine learning frameworks. - Experience in Python, or another language; command line usage; familiarity with Linux and AWS ecosystems. - Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc. - Excellent communication skills (written & spoken) and ability to collaborate effectively in a distributed, cross-functional team setting. Preferred Qualifications: - Track record of diving into data to discover hidden patterns and conducting error/deviation analysis - Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations - The motivation to achieve results in a fast-paced environment. - Exceptional level of organization and strong attention to detail - Comfortable working in a fast paced, highly collaborative, dynamic work environment - Papers published in AI/ML venues of repute
IN, KA, Bengaluru
The Amazon Alexa AI team in India is seeking a talented, self-driven Applied Scientist to work on prototyping, optimizing, and deploying ML algorithms within the realm of Generative AI. Key responsibilities include: - Research, experiment and build Proof Of Concepts advancing the state of the art in AI & ML for GenAI. - Collaborate with cross-functional teams to architect and execute technically rigorous AI projects. - Thrive in dynamic environments, adapting quickly to evolving technical requirements and deadlines. - Engage in effective technical communication (written & spoken) with coordination across teams. - Conduct thorough documentation of algorithms, methodologies, and findings for transparency and reproducibility. - Publish research papers in internal and external venues of repute - Support on-call activities for critical issues Basic Qualifications: - Master’s or PhD in computer science, statistics or a related field - 2-7 years experience in deep learning, machine learning, and data science. - Proficiency in coding and software development, with a strong focus on machine learning frameworks. - Experience in Python, or another language; command line usage; familiarity with Linux and AWS ecosystems. - Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc. - Excellent communication skills (written & spoken) and ability to collaborate effectively in a distributed, cross-functional team setting. - Papers published in AI/ML venues of repute Preferred Qualifications: - Track record of diving into data to discover hidden patterns and conducting error/deviation analysis - Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations - The motivation to achieve results in a fast-paced environment. - Exceptional level of organization and strong attention to detail - Comfortable working in a fast paced, highly collaborative, dynamic work environment
IN, KA, Bengaluru
Amazon is investing heavily in building a world class advertising business and we are responsible for defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities. The ATT team, based in Bangalore, is responsible for ensuring that ads are relevant and is of good quality, leading to higher conversion for the sellers and providing a great experience for the customers. We deal with one of the world’s largest product catalog, handle billions of requests a day with plans to grow it by order of magnitude and use automated systems to validate tens of millions of offers submitted by thousands of merchants in multiple countries and languages. In this role, you will build and develop ML models to address content understanding problems in Ads. These models will rely on a variety of visual and textual features requiring expertise in both domains. These models need to scale to multiple languages and countries. You will collaborate with engineers and other scientists to build, train and deploy these models. As part of these activities, you will develop production level code that enables moderation of millions of ads submitted each day.
US, WA, Seattle
The Search Supply & Experiences team, within Sponsored Products, is seeking an Applied Scientist to solve challenging problems in natural language understanding, personalization, and other areas using the latest techniques in machine learning. In our team, you will have the opportunity to create new ads experiences that elevate the shopping experience for our hundreds of millions customers worldwide. As an Applied Scientist, you will partner with other talented scientists and engineers to design, train, test, and deploy machine learning models. You will be responsible for translating business and engineering requirements into deliverables, and performing detailed experiment analysis to determine how shoppers and advertisers are responding to your changes. We are looking for candidates who thrive in an exciting, fast-paced environment and who have a strong personal interest in learning, researching, and creating new technologies with high customer impact. Key job responsibilities As an Applied Scientist on the Search Supply & Experiences team you will: - Perform hands-on analysis and modeling of enormous datasets to develop insights that increase traffic monetization and merchandise sales, without compromising the shopper experience. - Drive end-to-end machine learning projects that have a high degree of ambiguity, scale, and complexity. - Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models. - Design and run experiments, gather data, and perform statistical analysis. - Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. - Stay up to date on the latest advances in machine learning. About the team We are a customer-obsessed team of engineers, technologists, product leaders, and scientists. We are focused on continuous exploration of contexts and creatives where advertising delivers value to shoppers and advertisers. We specifically work on new ads experiences globally with the goal of helping shoppers make the most informed purchase decision. We obsess about our customers and we are continuously innovating on their behalf to enrich their shopping experience on Amazon
US, WA, Seattle
Amazon.com strives to be Earth's most customer-centric company where customers can shop in our stores to find and discover anything they want to buy. We hire the world's brightest minds, offering them a fast paced, technologically sophisticated and friendly work environment. Economists at Amazon partner closely with senior management, business stakeholders, scientist and engineers, and economist leadership to solve key business problems ranging from Amazon Web Services, Kindle, Prime, inventory planning, international retail, third party merchants, search, pricing, labor and employment planning, effective benefits (health, retirement, etc.) and beyond. Amazon Economists build econometric models using our world class data systems and apply approaches from a variety of skillsets – applied macro/time series, applied micro, econometric theory, empirical IO, empirical health, labor, public economics and related fields are all highly valued skillsets at Amazon. You will work in a fast moving environment to solve business problems as a member of either a cross-functional team embedded within a business unit or a central science and economics organization. You will be expected to develop techniques that apply econometrics to large data sets, address quantitative problems, and contribute to the design of automated systems around the company. About the team The International Seller Services (ISS) Economics team is a dynamic group at the forefront of shaping Amazon's global seller ecosystem. As part of ISS, we drive innovation and growth through sophisticated economic analysis and data-driven insights. Our mission is critical: we're transforming how Amazon empowers millions of international sellers to succeed in the digital marketplace. Our team stands at the intersection of innovative technology and practical business solutions. We're leading Amazon's transformation in seller services through work with Large Language Models (LLMs) and generative AI, while tackling fundamental questions about seller growth, marketplace dynamics, and operational efficiency. What sets us apart is our unique blend of rigorous economic methodology and practical business impact. We're not just analyzing data – we're building the frameworks and measurement systems that will define the future of Amazon's seller services. Whether we're optimizing the seller journey, evaluating new technologies, or designing innovative service models, our team transforms complex economic challenges into actionable insights that drive real-world results. Join us in shaping how millions of businesses worldwide succeed on Amazon's marketplace, while working on problems that combine economic theory, advanced analytics, and innovative technology.