Newsletter
Social
Conversational AI

A quick guide to Amazon's 40+ papers at EMNLP 2022

Familiar topics like information extraction and question answering share space with robotics and geolocation learning, and query rewriting emerges as a dynamic area of research.

By Staff writer
December 6, 2022
Share

Amazon’s more than 40 papers at this year’s Conference on Empirical Methods in Natural-Language Processing (EMNLP) — including papers accepted to EMNLP’s new industry track — cover some familiar topics, such as natural-language understanding and question answering. But they also wander farther afield, taking in such disparate subjects as robotics and geospatial learning — and two of the papers concern pun generation.

Query rewriting, whose applications include self-learning and reference resolution, has emerged as a dynamic area of research at Amazon, with five related papers at this year’s EMNLP. And several papers explore the burgeoning field of prompt engineering, or priming large language models to produce the desired types of output.

Below is a quick guide to Amazon’s EMNLP papers, both academic track and industry track.

Continual learning

Iterative stratified testing and measurement for automated model updates
Elizabeth Dekeyser, Nicholas Comment, Shermin Pei, Rajat Kumar, Shruti Rai, Fengtao Wu, Lisa Haverty, Kanna Shimizu

Towards need-based spoken language understanding model updates: What have we learned?
Quynh Do, Judith Gaspers, Daniil Sorokin, Patrick Lehnen

Unsupervised training data reweighting for natural language understanding with local distribution approximation
Jose Garrido Ramas, Thu Le, Bei Chen, Manoj Kumar, Kay Rottmann

Dialogue

retrieval-based-response.png
"Deploying a retrieval based response model for task oriented dialogues" proposes a model in which cross-attention layers learn the semantic correlations between history, profile features, and candidate responses, and a score function computes and ranks the candidate responses.

Deploying a retrieval based response model for task oriented dialogues
Lahari Poddar, Gyuri Szarvas, Cheng Wang, Georges Balazs, Pavel Danchenko, Patrick Ernst

Dialogue meaning representation for task-oriented dialogue systems
Xiangkun Hu, Junqi Dai, Hang Yan, Yi Zhang, Qipeng Guo, Xipeng Qiu, Zheng Zhang

Injecting domain knowledge in language models for task-oriented dialogue systems
Denis Emelin, Daniele Bonadiman, Sawsan Alqahtani, Yi Zhang, Saab Mansour

Evaluation

GEMv2: Multilingual NLG benchmarking in a single line of code
Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran, Alex Wang, Alexandros Papangelis, Aman Madaan, Angelina McMillan-Major, Anna Shvets, Ashish Upadhyay, Bernd Bohnet, Bingsheng Yao, Bryan Wilie, Chandra Bhagavatula, Chaobin You, Craig Thomson, Cristina Garbacea, Dakuo Wang, Daniel Deutsch, Deyi Xiong, Di Jin, Dimitra Gkatzia, Dragomir Radev, Elizabeth Clark, Esin Durmus, Faisal Ladhak, Filip Ginter, Genta Indra Winata, Hendrik Strobelt, Jekaterina Novikova, Jenna Kanerva, Jenny Chim, Jiawei Zhou, Jordan Clive, Joshua Maynez, João Sedoc, Juraj Juraska, Kaustubh Dhole, Khyathi Raghavi Chandu, Laura Perez-Beltrachini, Leonardo F. R. Ribeiro, Lewis Tunstall, Li Zhang, Mahima Pushkarna, Mathias Creutz, Michael White, Mihir Sanjay Kale, Moussa Kamal Eddine, Nico Daheim, Nishant Subramani, Ondrej Dusek, Paul Pu Liang, Pawan Sasanka Ammanamanch, Qi Zhu, Ratish Puduppully, Reno Kriz, Rifat Shahriyar, Saad Mahamood, Salomey Osei, Samuel Cahyawijaya, Sanja Štajner, Sebastien Montella, Shailza Jolly, Simon Mille, Tianhao Shen, Tosin Adewumi, Vikas Raunak, Vipul Raheja, Vitaly Nikolaev, Vivian Tsai, Yacine Jernite, Ying Xu, Yisi Sang, Yixin Liu, Yufang Hou

Fact verification

Fact checking machine generated text with dependency trees
Alex Estes, Nikhita Vedula, Marcus D. Collins, Matthew Cecil, Oleg Rokhlenko

Fact checking.png
The method proposed in "Fact checking machine generated text with dependency trees" identifies entity attributes from the dependency parse tree of an input claim whose factuality is to be assessed.

Fairness

MT-GenEval: A counterfactual and contextual dataset for evaluating gender accuracy in machine translation
Anna Currey, Maria Nadejde, Raghavendra Pappagari, Mia Mayer, Stanislas Lauly, Xing Niu, Benjamin Hsu, Georgiana Dinu

Humor

Context-situated pun generation
Jiao Sun, Anjali Narayan-Chen, Shereen Oraby, Shuyang Gao, Tagyoung Chung, Jing Huang, Yang Liu, Nanyun Peng

ExPUNations: Augmenting puns with keywords and explanations
Jiao Sun, Anjali Narayan-Chen, Shereen Oraby, Alessandra Cervone, Tagyoung Chung, Jing Huang, Yang Liu, Nanyun Peng

Information extraction

A hybrid approach to cross-lingual product review summarization
Saleh Soltan, Victor Soto, Ke Tran, Wael Hamza

Ask-and-Verify: Span candidate generation and verification for attribute value extraction
Yifan Ding, Yan Liang, Nasser Zalmout, Xian Li, Christan Grant, Tim Weninger

DORE: Document ordered relation extraction based on generative framework
Qipeng Guo, Yuqing Yang, Hang Yan, Xipeng Qiu, Zheng Zhang

DORE.png
The method proposed in "DORE: Document ordered relation extraction based on generative framework" identifies multiple instances of the same entity in an input document and builds a relation matrix that records relations between entities.

Learning to revise references for faithful summarization
Griffin Adams, Han-Chin Shing, Qing Sun, Christopher Winestock, Kathleen McKeown, Noémie Elhadad

Prototype-representations for training data filtering in weakly-supervised information extraction
Nasser Zalmout, Xian Li

Information retrieval

Accelerating learned sparse indexes via term impact decomposition
Joel Mackenzie, Antonio Mallia, Alistair Moffat, Matthias Petri

Machine translation impact in e-commerce multilingual search
Bryan Zhang, Amita Misra

Knowledge distillation

Distilling multilingual transformers into CNNs for scalable intent classification
Besnik Fetahu, Akash Veeragouni, Oleg Rokhlenko, Shervin Malmasi

Knowledge distillation transfer sets and their impact on downstream NLU tasks
Charith Peris, Lizhen Tan, Thomas Gueudre, Turan Gojayev, Pan Wei, Gokmen Oz

Machine learning

Calibrating imbalanced classifiers with focal loss: An empirical study
Cheng Wang, Georges Balazs, Gyuri Szarvas, Patrick Ernst, Lahari Poddar, Pavel Danchenko

Model adaptation

Meta-learning the difference: Preparing large language models for efficient adaptation
Zejiang Hou, Julian Salazar, George Polovets

Open world.png
In "Open world classification with adaptive negative samples", Amazon researchers propose a new method for discriminating known and open (unknown) categories of data. This figure compares their approach (d) to ordinary supervised learning (a) and an adaptive-decision-boundary method (c).

Open world classification with adaptive negative samples
Ke Bai, Guoyin Wang, Jiwei Li, Sunghyun Park, Sungjin Lee, Puyang Xu, Ricardo Henao, Lawrence Carin

Multimodal interaction

Multimodal context carryover
Prashan Wanigasekara, Nalin Gupta, Fan Yang, Emre Barut, Zeynab Raeesy, Kechen Qin, Stephen Rawls, Xinyue Liu, Chengwei Su, Spurthi Sandiri

Natural-language processing

McPhraSy: Multi context phrase similarity and clustering
Amir DN Cohen, Hila Gonen, Ori Shapira, Ran Levy, Yoav Goldberg

Unsupervised syntactically controlled paraphrase generation with abstract meaning representations
Kuan-Hao Huang, Varun Iyer, Anoop Kumar, Sriram Venkatapathy, Kai-Wei Chang, Aram Galstyan

Natural-language understanding

Improving large-scale conversational assistants using model interpretation based training sample selection
Stefan Schroedl, Manoj Kumar, Kiana Hajebi, Morteza Ziyadi, Sriram Venkatapathy, Anil Ramakrishna, Rahul Gupta, Pradeep Natarajan

Improving text-to-SQL semantic parsing with fine-grained query understanding
Jun Wang, Patrick Ng, Alexander Hanbo Li, Jiarong Jiang, Zhiguo Wang, Ramesh Nallapati, Bing Xiang, Sudipta Sengupta

Learning geolocations for cold-start and hard-to-resolve addresses via deep metric learning
Govind, Saurabh Sohoney

Geolocation.png
"Learning geolocations for cold-start and hard-to-resolve addresses via deep metric learning" proposes a way to use deep metric learning on addresses to capture geospatial distance semantics.

Prompt engineering

DynaMaR: Dynamic prompt with mask token representation
Xiaodi Sun, Sunny Rajagopalan, Priyanka Nigam, Weiyi Lu, Yi Xu, Iman Keivanloo, Belinda Zeng, Trishul Chilimbi

Inducer-tuning: Connecting prefix-tuning and adapter-tuning
Yifan Chen, Devamanyu Hazarika, Mahdi Namazifar, Yang Liu, Di Jin, Dilek Hakkani-Tür

Query rewriting

CGF.png
In "CGF: Constrained generation framework for query rewriting in conversational AI", Amazon researchers use tries — trees in which each node extends a text by one word — to constrain the outputs of a model that generates query rewrites.

CGF: Constrained generation framework for query rewriting in conversational AI
Jie Hao, Yang Liu, Xing Fan, Saurabh Gupta, Saleh Soltan, Rakesh Chada, Pradeep Natarajan, Edward Guo, Gokhan Tur

CycleKQR: Unsupervised bidirectional keyword question rewriting
Andrea Iovine, Anjie Fang, Besnik Fetahu, Jie Zhao, Oleg Rokhlenko, Shervin Malmasi

PAIGE: Personalized adaptive interactions graph encoder for query rewriting in dialogue systems
Daniel Bis, Saurabh Gupta, Jie Hao, Xing Fan, Edward Guo

PENTATRON: PErsonalized coNText-aware transformer for retrieval-based cOnversational uNderstanding
Niranjan Uma Naresh, Ziyan Jiang, Ankit, Sungjin Lee, Jie Hao, Xing Fan, Edward Guo

Reinforced question rewriting for conversational question answering
Zhiyu Chen, Jie Zhao, Anjie Fang, Besnik Fetahu, Oleg Rokhlenko, Shervin Malmasi

Question answering

Ensemble transformer for efficient and accurate ranking tasks: An application to question answering systems
Yoshitomo Matsubara, Luca Soldaini, Eric Lind, Alessandro Moschitti

FocusQA: Open-domain question answering with a context in focus
Gianni Barlacchi, Ivano Lauriola, Alessandro Moschitti, Marco Del Tredici, Xiaoyu Shen, Thuy Vu, Bill Byrne, Adrià de Gispert

Knowledge transfer from answer ranking to answer generation
Matteo Gabburo, Rik Koncel-Kedziorski, Siddhant Garg, Luca Soldaini, Alessandro Moschitti

Pre-training transformer models with sentence-level objectives for answer sentence selection
Luca Di Liello, Siddhant Garg, Luca Soldaini, Alessandro Moschitti

RLET: A reinforcement learning based approach for explainable QA with entailment trees
Tengxiao Liu, Qipeng Guo, Xiangkun Hu, Yue Zhang, Xipeng Qiu, Zheng Zhang

Robotics

ALFRED-L: Investigating the role of language for action learning in interactive visual environments
Arjun R. Akula, Spandana Gella, Aishwarya Padmakumar, Mahdi Namazifar, Mohit Bansal, Jesse Thomason, Dilek Hakkani-Tür

ALFRED-L.png
"ALFRED-L: Investigating the role of language for action learning in interactive visual environments" proposes a new test split to the ALFRED benchmark for embodied-task completion. The test split — ALFRED-L — includes instructions that an agent backtrack to known reference positions along its trajectory, to evaluate whether it can remember their locations.

Research areas
Tags
About the Author
Staff writer

Related content

Work with us

See more jobs See more jobs
Applied Scientist, Artificial General Intelligence
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.
Applied Scientist III, Amzn Shipping-Prd & Tech, Amzn Shipping-Prd & Tech
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!
Senior Applied Scientist, FireTV
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.
Applied Scientist, Artificial General Intelligence
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.
Applied Scientist II, Alexa AI
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
Applied Scientist II, Advertising Trust
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.
Applied Scientist , Sponsored Products
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
Applied Scientist, Translation Services
US, WA, Seattle
Have you ever wondered how Amazon launches and maintains a consistent customer experience across hundreds of countries and languages it serves its customers? Are you passionate about data and mathematics, and hope to impact the experience of millions of customers? Are you obsessed with designing simple algorithmic solutions to very challenging problems? If so, we look forward to hearing from you! At Amazon, we strive to be Earth's most customer-centric company, where both internal and external customers can find and discover anything they want in their own language of preference. Our Translations Services (TS) team plays a pivotal role in expanding the reach of our marketplace worldwide and enables thousands of developers and other stakeholders (Product Managers, Program Managers, Linguists) in developing locale specific solutions. Amazon Translations Services (TS) is seeking an Applied Scientist to be based in our Seattle office. As a key member of the Science and Engineering team of TS, this person will be responsible for designing algorithmic solutions based on data and mathematics for translating billions of words annually across 130+ and expanding set of locales. The successful applicant will ensure that there is minimal human touch involved in any language translation and accurate translated text is available to our worldwide customers in a streamlined and optimized manner. With access to vast amounts of data, cutting-edge technology, and a diverse community of talented individuals, you will have the opportunity to make a meaningful impact on the way customers and stakeholders engage with Amazon and our platform worldwide. Together, we will drive innovation, solve complex problems, and shape the future of e-commerce. Key job responsibilities * Apply your expertise in LLM models to design, develop, and implement scalable machine learning solutions that address complex language translation-related challenges in the eCommerce space. * Collaborate with cross-functional teams, including software engineers, data scientists, and product managers, to define project requirements, establish success metrics, and deliver high-quality solutions. * Conduct thorough data analysis to gain insights, identify patterns, and drive actionable recommendations that enhance seller performance and customer experiences across various international marketplaces. * Continuously explore and evaluate state-of-the-art modeling techniques and methodologies to improve the accuracy and efficiency of language translation-related systems. * Communicate complex technical concepts effectively to both technical and non-technical stakeholders, providing clear explanations and guidance on proposed solutions and their potential impact. About the team We are a start-up mindset team. As the long-term technical strategy is still taking shape, there is a lot of opportunity for this fresh Science team to innovate by leveraging Gen AI technoligies to build scalable solutions from scratch. Our Vision: Language will not stand in the way of anyone on earth using Amazon products and services. Our Mission: We are the enablers and guardians of translation for Amazon's customers. We do this by offering hands-off-the-wheel service to all Amazon teams, optimizing translation quality and speed at the lowest cost possible.
Economist, International Seller Service
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.
Data Scientist, AWS Industries
GB, London
Are you looking to work at the forefront of Machine Learning and AI? Would you be excited to apply cutting edge Generative AI algorithms to solve real world problems with significant impact? The AWS Industries Team at AWS helps AWS customers implement Generative AI solutions and realize transformational business opportunities for AWS customers in the most strategic industry verticals. This is a team of data scientists, engineers, and architects working step-by-step with customers to build bespoke solutions that harness the power of generative AI. The team helps customers imagine and scope the use cases that will create the greatest value for their businesses, select and train and fine tune the right models, define paths to navigate technical or business challenges, develop proof-of-concepts, and build applications to launch these solutions at scale. The AWS Industries team provides guidance and implements best practices for applying generative AI responsibly and cost efficiently. You will work directly with customers and innovate in a fast-paced organization that contributes to game-changing projects and technologies. You will design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. In this Data Scientist role you will be capable of using GenAI and other techniques to design, evangelize, and implement and scale cutting-edge solutions for never-before-solved problems. Key job responsibilities - Collaborate with AI/ML scientists, engineers, and architects to research, design, develop, and evaluate cutting-edge generative AI algorithms and build ML systems to address real-world challenges - Interact with customers directly to understand the business problem, help and aid them in implementation of generative AI solutions, deliver briefing and deep dive sessions to customers and guide customer on adoption patterns and paths to production - Create and deliver best practice recommendations, tutorials, blog posts, publications, sample code, and presentations adapted to technical, business, and executive stakeholder - Provide customer and market feedback to Product and Engineering teams to help define product direction About the team Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship and Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.
See more jobs
View from space of a connected network around planet Earth representing the Internet of Things.
Get more from Amazon Science
Subscribe to our monthly newsletter

Amazon.com | Conditions of Use | Privacy | © 1996-2025 Amazon.com, Inc. or its affiliates

Social