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

US, CA, Palo Alto
Join a team working on cutting-edge science to innovate search experiences for Amazon shoppers! Amazon Search helps customers shop with ease, confidence and delight WW. We aim to transform Search from an information retrieval engine to a shopping engine. In this role, you will build models to generate and recommend search queries that can help customers fulfill their shopping missions, reduce search efforts and let them explore and discover new products. You will also build models and applications that will increase customer awareness of related products and product attributes that might be best suited to fulfill the customer needs. Key job responsibilities On a day-to-day basis, you will: Design, develop, and evaluate highly innovative, scalable models and algorithms; Design and execute experiments to determine the impact of your models and algorithms; Work with product and software engineering teams to manage the integration of successful models and algorithms in complex, real-time production systems at very large scale; Share knowledge and research outcomes via internal and external conferences and journal publications; Project manage cross-functional Machine Learning initiatives. About the team The mission of Search Assistance is to improve search feature by reducing customers’ effort to search. We achieve this through three customer-facing features: Autocomplete, Spelling Correction and Related Searches. The core capability behind the three features is backend service Query Recommendation.
US, CA, Palo Alto
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 Ad Response Prediction team in Sponsored Products organization build advanced deep-learning models, large-scale machine-learning (ML) 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 energetic, entrepreneurial, and self-driven science leaders to join the team. Key job responsibilities As a Principal Applied Scientist in the team, you will: Seek to understand in depth the Sponsored Products offering at Amazon and identify areas of opportunities to grow our business via principled ML solutions. Mentor and guide the applied scientists in our organization and hold us to a high standard of technical rigor and excellence in ML. Design and lead organization wide ML roadmaps to help our Amazon shoppers have a delightful shopping experience while creating long term value for our sellers. Work with our engineering partners and draw upon your experience to meet latency and other system constraints. Identify untapped, high-risk technical and scientific directions, and simulate new research directions that you will drive to completion and deliver. Be responsible for communicating our ML innovations to the broader internal & external scientific community.
US, CA, Santa Clara
AWS AI/ML is looking for world class scientists and engineers to join its AI Research and Education group working on foundation models, large-scale representation learning, and distributed learning methods and systems. At AWS AI/ML you will invent, implement, and deploy state of the art machine learning algorithms and systems. You will build prototypes and innovate on new representation learning solutions. You will interact closely with our customers and with the academic and research communities. You will be at the heart of a growing and exciting focus area for AWS and work with other acclaimed engineers and world famous scientists. Large-scale foundation models have been the powerhouse in many of the recent advancements in computer vision, natural language processing, automatic speech recognition, recommendation systems, and time series modeling. Developing such models requires not only skillful modeling in individual modalities, but also understanding of how to synergistically combine them, and how to scale the modeling methods to learn with huge models and on large datasets. Join us to work as an integral part of a team that has diverse experiences in this space. We actively work on these areas: * Hardware-informed efficient model architecture, training objective and curriculum design * Distributed training, accelerated optimization methods * Continual learning, multi-task/meta learning * Reasoning, interactive learning, reinforcement learning * Robustness, privacy, model watermarking * Model compression, distillation, pruning, sparsification, quantization About Us Inclusive Team Culture Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Work/Life Balance Our team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives. Mentorship & Career Growth Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.
US, CA, Palo Alto
We’re working to improve shopping on Amazon using the conversational capabilities of large language models, and are searching for pioneers who are passionate about technology, innovation, and customer experience, and are ready to make a lasting impact on the industry. You'll be working with talented scientists, engineers, and technical program managers (TPM) to innovate on behalf of our customers. If you're fired up about being part of a dynamic, driven team, then this is your moment to join us on this exciting journey!"?
US, WA, Seattle
Do you want to join an innovative team of scientists who use machine learning to help Amazon provide the best experience to our Selling Partners by automatically understanding and addressing their challenges, needs and opportunities? Do you want to build advanced algorithmic systems that are powered by state-of-art ML, such as Natural Language Processing, Large Language Models, Deep Learning, Computer Vision and Causal Modeling, to seamlessly engage with Sellers? Are you excited by the prospect of analyzing and modeling terabytes of data and creating cutting edge algorithms to solve real world problems? Do you like to build end-to-end business solutions and directly impact the profitability of the company and experience of our customers? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Selling Partner Experience Science team. Key job responsibilities Use statistical and machine learning techniques to create the next generation of the tools that empower Amazon's Selling Partners to succeed. Design, develop and deploy highly innovative models to interact with Sellers and delight them with solutions. Work closely with teams of scientists and software engineers to drive real-time model implementations and deliver novel and highly impactful features. Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation. Research and implement novel machine learning and statistical approaches. Lead strategic initiatives to employ the most recent advances in ML in a fast-paced, experimental environment. Drive the vision and roadmap for how ML can continually improve Selling Partner experience. About the team Selling Partner Experience Science (SPeXSci) is a growing team of scientists, engineers and product leaders engaged in the research and development of the next generation of ML-driven technology to empower Amazon's Selling Partners to succeed. We draw from many science domains, from Natural Language Processing to Computer Vision to Optimization to Economics, to create solutions that seamlessly and automatically engage with Sellers, solve their problems, and help them grow. Focused on collaboration, innovation and strategic impact, we work closely with other science and technology teams, product and operations organizations, and with senior leadership, to transform the Selling Partner experience.
US, WA, Seattle
The AWS AI Labs team has a world-leading team of researchers and academics, and we are looking for world-class colleagues to join us and make the AI revolution happen. Our team of scientists have developed the algorithms and models that power AWS computer vision services such as Amazon Rekognition and Amazon Textract. As part of the team, we expect that you will develop innovative solutions to hard problems, and publish your findings at peer reviewed conferences and workshops. AWS is the world-leading provider of cloud services, has fostered the creation and growth of countless new businesses, and is a positive force for good. Our customers bring problems which will give Applied Scientists like you endless opportunities to see your research have a positive and immediate impact in the world. You will have the opportunity to partner with technology and business teams to solve real-world problems, have access to virtually endless data and computational resources, and to world-class engineers and developers that can help bring your ideas into the world. Our research themes include, but are not limited to: few-shot learning, transfer learning, unsupervised and semi-supervised methods, active learning and semi-automated data annotation, large scale image and video detection and recognition, face detection and recognition, OCR and scene text recognition, document understanding, 3D scene and layout understanding, and geometric computer vision. For this role, we are looking for scientist who have experience working in the intersection of vision and language. We are located in Seattle, Pasadena, Palo Alto (USA) and in Haifa and Tel Aviv (Israel).
GB, London
Are you excited about applying economic models and methods using large data sets to solve real world business problems? Then join the Economic Decision Science (EDS) team. EDS is an economic science team based in the EU Stores business. The teams goal is to optimize and automate business decision making in the EU business and beyond. An internship at Amazon is an opportunity to work with leading economic researchers on influencing needle-moving business decisions using incomparable datasets and tools. It is an opportunity for PhD students and recent PhD graduates in Economics or related fields. 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, as well as basic familiarity with Stata, R, or Python is necessary. Experience with SQL would be a plus. As an Economics Intern, you will be working in a fast-paced, cross-disciplinary team of researchers who are pioneers in the field. You will take on complex problems, and work on solutions that either leverage existing academic and industrial research, or utilize your own out-of-the-box pragmatic thinking. In addition to coming up with novel solutions and prototypes, you may even need to deliver these to production in customer facing products. Roughly 85% of previous intern cohorts have converted to full time economics employment at Amazon.
US, CA, Cupertino
We're looking for an Applied Scientist to help us secure Amazon's most critical data. In this role, you'll work closely with internal security teams to design and build AR-powered systems that protect our customers' data. You will build on top of existing formal verification tools developed by AWS and develop new methods to apply those tools at scale. You will need to be innovative, entrepreneurial, and adaptable. We move fast, experiment, iterate and then scale quickly, thoughtfully balancing speed and quality. Inclusive Team Culture Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Work/Life Balance Our team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives. Mentorship & Career Growth Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future. Key job responsibilities Deeply understand AR techniques for analyzing programs and other systems, and keep up with emerging ideas from the research community. Engage with our customers to develop understanding of their needs. Propose and develop solutions that leverage symbolic reasoning services and concepts from programming languages, theorem proving, formal verification and constraint solving. Implement these solutions as services and work with others to deploy them at scale across Payments and Healthcare. Author papers and present your work internally and externally. Train new teammates, mentor others, participate in recruiting and interviewing, and participate in our tactical and strategic planning. About the team Our small team of applied scientists works within a larger security group, supporting thousands of engineers who are developing Amazon's payments and healthcare services. Security is a rich area for automated reasoning. Most other approaches are quite ad-hoc and take a lot of human effort. AR can help us to reason deliberately and systematically, and the dream of provable security is incredibly compelling. We are working to make this happen at scale. We partner closely with our larger security group and with other automated reasoning teams in AWS that develop core reasoning services.
US, NY, New York
Search Thematic Ad Experience (STAX) team within Sponsored Products is looking for a leader to lead a team of talented applied scientists working on cutting-edge science to innovate on ad experiences for Amazon shoppers!. You will manage a team of scientists, engineers, and PMs to innovate new widgets on Amazon Search page to improve shopper experience using state-of-the-art NLP and computer vision models. You will be leading some industry first experiences that has the potential to revolutionize how shopping looks and feels like on Amazon, and e-commerce marketplaces in general. You will have the opportunity to design the vision on how ad experiences look on Amazon search page, and use the combination of advanced techniques and continuous experimentation to realize this vision. Your work will be core to Amazon’s advertising business. You will be a significant contributor in building the future of sponsored advertising, directly impacting the shopper experience for our hundreds of millions of shoppers worldwide, while delivering significant value for hundreds of thousands of advertisers across the purchase journey with ads on Amazon. Key job responsibilities * Be the technical leader in Machine Learning; lead efforts within the team, and collaborate and influence across the organization. * Be a critic, visionary, and execution leader. Invent and test new product ideas that are powered by science that addresses key product gaps or shopper needs. * Set, plan, and execute on a roadmap that strikes the optimal balance between short term delivery and long term exploration. You will influence what we invest in today and tomorrow. * Evangelize the team’s science innovation within the organization, company, and in key conferences (internal and external). * Be ruthless with prioritization. You will be managing a team which is highly sought after. But not all can be done. Have a deep understanding of the tradeoffs involved and be fierce in prioritizing. * Bring clarity, direction, and guidance to help teams navigate through unsolved problems with the goal to elevate the shopper experience. We work on ambiguous problems and the right approach is often unknown. You will bring your rich experience to help guide the team through these ambiguities, while working with product and engineering in crisply defining the science scope and opportunities. * Have strong product and business acumen to drive both shopper improvements and business outcomes. A day in the life * Lead a multidisciplinary team that embodies “customer obsessed science”: inventing brand new approaches to solve Amazon’s unique problems, and using those inventions in software that affects hundreds of millions of customers * Dive deep into our metrics, ongoing experiments to understand how and why they are benefitting our shoppers (or not) * Design, prototype and validate new widgets, techniques, and ideas. Take end-to-end ownership of moving from prototype to final implementation. * Be an advocate and expert for STAX science to leaders and stakeholders inside and outside advertising. About the team We are the Search thematic ads experience team within Sponsored products - a fast growing team of customer-obsessed engineers, technologists, product leaders, and scientists. We are focused on continuous exploration of contexts and creatives to drive value for both our customers and advertisers, through continuous innovation. We focus on new ads experiences globally to help shoppers make the most informed purchase decision while helping shortcut the time to discovery that shoppers are highly likely to engage with. We also harvest rich contextual and behavioral signals that are used to optimize our backend models to continually improve the shopper experience. We obsess about our customers and are continuously seeking opportunities to delight them.
US, CA, Palo Alto
Amazon is the 4th most popular site in the US. Our product search engine, one of the most heavily used services in the world, indexes billions of products and serves hundreds of millions of customers world-wide. We are working on a new initiative to transform our search engine into a shopping engine that assists customers with their shopping missions. We look at all aspects of search CX, query understanding, Ranking, Indexing and ask how we can make big step improvements by applying advanced Machine Learning (ML) and Deep Learning (DL) techniques. We’re seeking a thought leader to direct science initiatives for the Search Relevance and Ranking at Amazon. This person will also be a deep learning practitioner/thinker and guide the research in these three areas. They’ll also have the ability to drive cutting edge, product oriented research and should have a notable publication record. This intellectual thought leader will help enhance the science in addition to developing the thinking of our team. This leader will direct and shape the science philosophy, planning and strategy for the team, as we explore multi-modal, multi lingual search through the use of deep learning . We’re seeking an individual that can enhance the science thinking of our team: The org is made of 60+ applied scientists, (2 Principal scientists and 5 Senior ASMs). This person will lead and shape the science philosophy, planning and strategy for the team, as we push into Deep Learning to solve problems like cold start, discovery and personalization in the Search domain. Joining this team, you’ll experience the benefits of working in a dynamic, entrepreneurial environment, while leveraging the resources of Amazon [Earth's most customer-centric internet company]. We provide a highly customer-centric, team-oriented environment in our offices located in Palo Alto, California.