Subscribe
Follow Us
Conversational AI / Natural-language processing

A quick guide to Amazon’s 45-plus NAACL papers

The breadth and originality of Amazon’s natural-language-processing research are on display at the annual meeting of the North American chapter of the Association for Computational Linguistics.

By Staff writer
July 07, 2022
Share

Amazon’s 45-plus papers at the annual meeting of the North American chapter of the Association for Computational Linguistics, which begins next week, sorted by research area.

Continual learning

Lifelong pretraining: Continually adapting language models to emerging corpora
Xisen Jin, Dejiao Zhang, Henghui Zhu, Wei Xiao, Shang-Wen Li, Xiaokai Wei, Andrew O. Arnold, Xiang Ren

Local-to-global learning for iterative training of production SLU models on new features
Yulia Grishina, Daniil Sorokin

Overcoming catastrophic forgetting during domain adaptation of seq2seq language generation
Dingcheng Li, Zheng Chen, Eunah Cho, Jie Hao, Xiaohu Liu, Xing Fan, Chenlei (Edward) Guo, Yang Liu

Overcoming catastrophic forgetting.png
In "Overcoming catastrophic forgetting during domain adaptation of seq2seq language generation", Amazon researchers propose a method for estimating how much data representations shift when an existing model is trained on a new task (right).

Temporal generalization for spoken language understanding
Judith Gaspers, Anoop Kumar, Greg Ver Steeg, Aram Galstyan

Data augmentation

Constraining word alignments with posterior regularization for label transfer
Kevin Martin Jose, Thomas Gueudré

Word alignments.png
An example of the difficulty in using word alignment to transfer textual labels from one language to another. In English, the article "the" is assigned the label "o", for "other"; in French, the abbreviated article is combined with its noun, and both receive the same label ("type"). From "Constraining word alignments with posterior regularization for label transfer".

Controlled data generation via insertion operations for NLU
Manoj Kumar, Haidar Khan, Yuval Merhav, Wael Hamza, Anna Rumshisky, Rahul Gupta

Efficient semi supervised consistency training for natural language understanding
George Leung, Joshua Tan

Learning to generate examples for semantic processing tasks
Danilo Croce, Simone Filice, Giuseppe Castellucci, Roberto Basili

Dialogue

Learning dialogue representations from consecutive utterances
Zhihan Zhou, Dejiao Zhang, Wei Xiao, Nicholas Dingwall, Xiaofei Ma, Andrew O. Arnold, Bing Xiang

Massive-scale decoding for text generation using lattices
Jiacheng Xu, Siddhartha Reddy Jonnalagadda, Greg Durrett

Entity linking, resolution, and typing

Contrastive representation learning for cross-document coreference resolution of events and entities
Benjamin Hsu, Graham Horwood

Improving entity disambiguation by reasoning over a knowledge base
Tom Ayoola, Joseph Fisher, Andrea Pierleoni

ReFinED: An efficient zero-shot-capable approach to end-to-end entity linking
Tom Ayoola, Shubhi Tyagi, Joseph Fisher, Christos Christodoulopoulos, Andrea Pierleoni

Instilling type knowledge in language models via multi-task QA
Shuyang Li, Mukund Sridhar, Chandana Satya Prakash, Jin Cao, Wael Hamza, Julian McAuley

Explainable AI

Entailment trees.png
In "Entailment tree explanations via iterative retrieval-generation reasoner", Amazon researchers propose a method for explaining the outputs of large language models by logically recombining premises extracted from supporting textual evidence.

Entailment tree explanations via iterative retrieval-generation reasoner
Danilo Neves Ribeiro, Shen Wang, Xiaofei Ma, Rui Dong, Xiaokai Wei, Henry Zhu, Xinchi Chen, Zhiheng Huang, Peng Xu, Andrew O. Arnold, Dan Roth

Locally aggregated feature attribution on natural language model understanding
Sheng Zhang, Jin Wang, Haitao Jiang, Rui Song

Extreme multilabel classification

Augmenting training data for massive semantic matching models in low-traffic e-commerce stores
Ashutosh Joshi, Shankar Vishwanath, Choon Hui Teo, Vaclav Petricek, Vishy Vishwanathan, Rahul Bhagat, Jonathan May

Extreme zero shot learning for extreme text classification
Yuanhao Xiong, Wei-Cheng Chang, Cho-Jui Hsieh, Hsiang-Fu Yu, Inderjit S. Dhillon

Federated learning

Federated learning with noisy user feedback
Rahul Sharma, Anil Ramakrishna, Ansel MacLaughlin, Anna Rumshisky, Jimit Majmudar, Clement Chung, Salman Avestimehr, Rahul Gupta

Keyword spotting

AB/BA analysis: A framework for estimating keyword spotting recall improvement while maintaining audio privacy
Raphael Petegrosso, Vasistakrishna Baderdinni, Thibaud Senechal, Benjamin L. Bullough

Machine translation

CoCoA-MT: A dataset and benchmark for contrastive controlled MT with application to formality
Maria Nadejde, Anna Currey, Benjamin Hsu, Xing Niu, Marcello Federico, Georgiana Dinu

Dynamic pulling.png
In federated learning, distributed copies of a neural network are trained locally, and only their updates (red) are sent to a central model. "Training mixed-domain translation models via federated learning" introduces a technique called dynamic pulling, in which distributed models with large shifts in parameter values between training rounds (lower left) see their parameters pulled into the central model separately from those of models with smaller shifts.

The devil is in the details: On the pitfalls of vocabulary selection in neural machine translation
Tobias Domhan, Eva Hasler, Ke Tran, Sony Trenous, Bill Byrne, Felix Hieber

Training mixed-domain translation models via federated learning
Peyman Passban, Tanya G. Roosta, Rahul Gupta, Ankit Chadha, Clement Chung

Multitask learning

Asynchronous convergence in multi-task learning via knowledge distillation from converged tasks
Weiyi Lu, Sunny Rajagopalan, Priyanka Nigam, Jaspreet Singh, Xiaodi Sun, Yi Xu, Belinda Zeng, Trishul Chilimbi

Exploring the role of task transferability in large-scale multi-task learning
Vishakh Padmakumar, Leonard Lausen, Miguel Ballesteros, Sheng Zha, He He, George Karypis

Named-entity recognition

Dynamic gazetteer integration in multilingual models for cross-lingual and cross-domain named entity recognition
Besnik Fetahu, Anjie Fang, Oleg Rokhlenko, Shervin Malmasi

NER-MQMRC: Formulating named entity recognition as multi question machine reading comprehension
Anubhav Shrimal, Avi Jain, Kartik Mehta, Promod Yenigalla

Question answering

Answer consolidation: Formulation and benchmarking
Wenxuan Zhou, Qiang Ning, Heba Elfardy, Kevin Small, Muhao Chen

Paragraph-based transformer pre-training for multi-sentence inference
Luca Di Liello, Siddhant Garg, Luca Soldaini, Alessandro Moschitti

PerKGQA: Question answering over personalized knowledge graphs
Ritam Dutt, Kasturi Bhattacharjee, Rashmi Gangadharaiah, Dan Roth, Carolyn Penstein Rosé

Product answer generation from heterogeneous sources: A new benchmark and best practices
Xiaoyu Shen, Gianni Barlacchi, Marco Del Tredici, Weiwei Cheng, Adria de Gispert, Bill Byrne

Recommender systems

CERES: Pretraining of graph-conditioned transformer for semi-structured session data
Rui Feng, Chen Luo, Qingyu Yin, Bing Yin, Tuo Zhao, Chao Zhang

Self-learning

Failure point isolation.png
In "FPI: Failure point isolation in large-scale conversational assistants", Amazon researchers propose a method for deducing where in a conversational agent's processing pipeline an error has occurred.

FPI: Failure point isolation in large-scale conversational assistants
Rinat Khaziev, Usman Shahid, Tobias Röding, Rakesh Chada, Emir Kapanci, Pradeep Natarajan

Scalable and robust self-learning for skill routing in large-scale conversational AI systems
Mohammad Kachuee, Jinseok Nam, Sarthak Ahuja, Jin-Myung Won, Sungjin Lee

Self-aware feedback-based self-learning in large-scale conversational AI
Pragaash Ponnusamy, Clint Solomon Mathialagan, Gustavo Aguilar, Chengyuan Ma, Chenlei (Edward) Guo

Task-oriented parsing.png
An example of task-oriented semantic parsing, which converts natural language into a formal representation that an AI agent can act on. From "Compositional task-oriented parsing as abstractive question answering".

Semantic parsing

Compositional task oriented parsing as abstractive question answering
Wenting Zhao, Konstantine Arkoudas, Weiqi Sun, Claire Cardie

SeqZero: Few-shot compositional semantic parsing with sequential prompts and zero-shot models
Jingfeng Yang, Haoming Jiang, Qingyu Yin, Danqing Zhang, Bing Yin, Diyi Yang

Task adaptation

Attention fusion: A light yet efficient late fusion mechanism for task adaptation in NLU
Jin Cao, Chandana Satya Prakash, Wael Hamza

Empowering parameter-efficient transfer learning by recognizing the kernel structure in attention
Yifan Chen, Devamanyu Hazarika, Mahdi Namazifar, Yang Liu, Di Jin, Dilek Hakkani-Tür

Text mining

Distantly supervised aspect clustering and naming for e-commerce reviews
Prateek Sircar, Aniket Chakrabarti, Deepak Gupta, Anirban Majumdar

Efficient few-shot fine-tuning for opinion summarization
Arthur Bražinskas, Ramesh Nallapati, Mohit Bansal, Markus Dreyer

FactGraph: Evaluating factuality in summarization with semantic graph representations
Leonardo F. R. Ribeiro, Mengwen Liu, Iryna Gurevych, Markus Dreyer, Mohit Bansal

Knowledge selection.png
An example of how a conversational agent might incorporate facts gleaned form online sources (white boxes) into its conversational replies (blue boxes). From "Enhanced knowledge selection for grounded dialogues via document semantic graphs".

Enhanced knowledge selection for grounded dialogues via document semantic graphs
Sha Li, Madhi Namazifar, Di Jin, Mohit Bansal, Heng Ji, Yang Liu, Dilek Hakkani-Tür

Retrieval-augmented multilingual keyphrase generation with retriever-generator iterative training
Yifan Gao, Qingyu Yin, Zheng Li, Rui Meng, Tong Zhao, Bing Yin, Irwin King, Michael R. Lyu

What do users care about? Detecting actionable insights from user feedback
Kasturi Bhattacharjee, Rashmi Gangadharaiah, Kathleen McKeown, Dan Roth

Text-to-speech

Empathic machines: using intermediate features as levers to emulate emotions in text-to-speech systems
Saiteja Kosgi, Sarath Sivaprasad, Niranjan Pedanekar, Anil Nelakanti, Vineet Gandhi

Research areas
Tags
About the Author
Staff writer

Related content

Work with us

See more jobs See more jobs
Applied Scientist
GB, Cambridge
Our team undertakes research together with multiple organizations to advance the state-of-the-art in speech technologies. We not only work on giving Alexa, the ground-breaking service that powers Echo, her voice, but we also develop cutting-edge technologies with Amazon Studios, the provider of original content for Prime Video. Do you want to be part of the team developing the latest technology that impacts the customer experience of ground-breaking products? Then come join us and make history. We are looking for a passionate, talented, and inventive Senior Applied Scientist with a background in Machine Learning to help build industry-leading Speech, Language and Video technology. As a Senior Applied Scientist at Amazon you will work with talented peers to develop novel algorithms and modelling techniques to drive the state of the art in speech and vocal arts synthesis. Position Responsibilities: - Participate in the design, development, evaluation, deployment and updating of data-driven models for digital vocal arts applications. - Participate in research activities including the application and evaluation and digital vocal and video arts techniques for novel applications. - Research and implement novel ML and statistical approaches to add value to the business. - Mentor junior engineers and scientists. We are open to hiring candidates to work out of one of the following locations: Cambridge, GBR
Intern - Economics, PXT Central Science
US, WA, Seattle
The Amazon Economics Team is hiring Economist Interns. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets to solve real-world business problems. Some knowledge of econometrics, as well as basic familiarity with Stata, R, or Python is necessary. Experience with SQL, UNIX, Sawtooth, and Spark would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, data scientists and MBAʼs. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with future job market placement. Roughly 85% of interns from previous cohorts have converted to full-time economics employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
Senior Applied Scientist, Sponsored Products
US, NY, New York
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. We deliver billions of ad impressions and millions of clicks daily and break 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. Our systems and algorithms operate on one of the world's largest product catalogs, matching shoppers with advertised products with a high relevance bar and strict latency constraints. Sponsored Products Detail Page Blended Widgets team is chartered with building novel product recommendation experiences. We push the innovation frontiers for our hundreds of millions of customers WW to aid product discovery while helping shoppers to find relevant products easily. Our team is building differentiated recommendations that highlight specific characteristics of products (either direct attributes, inferred or machine learned), and leveraging generative AI to provide interactive shopping experiences. We are looking for a Senior Applied Scientist who can delight our customers by continually learning and inventing. Our ideal candidate is an experienced Applied Scientist who has a track-record of performing deep analysis and is passionate about applying advanced ML and statistical techniques to solve real-world, ambiguous and complex challenges to optimize and improve the product performance, and who is motivated to achieve results in a fast-paced environment. The position offers an exceptional opportunity to grow your technical and non-technical skills and make a real difference to the Amazon Advertising business. As a Senior Applied Scientist on this team, you will: * Be the technical leader in Machine Learning; lead efforts within this team and collaborate across teams * Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, perform hands-on analysis and modeling of enormous data sets to develop insights that improve shopper experiences and merchandise sales * Drive end-to-end Machine Learning projects that have a high degree of ambiguity, scale, 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. * Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. * Research new and innovative machine learning approaches. * Promote the culture of experimentation and applied science at Amazon Team video https://youtu.be/zD_6Lzw8raE We are also open to consider the candidate in Seattle, or Palo Alto. We are open to hiring candidates to work out of one of the following locations: New York, NY, USA
Senior Applied Scientist, Amazon
US, VA, Arlington
Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising products that drive discovery and sales. As a core product offering within our advertising portfolio, Sponsored Products (SP) helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The SP team's primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! The Search Sourcing and Relevance team parses billions of ads to surface the best ad to show to Amazon shoppers. The team strives to understand customer intent and identify relevant ads that enable them to discover new and alternate products. This also enables sellers on Amazon to showcase their products to customers, which may, at times, be buried deeper in the search results. By showing the right ads to customers at the right time, this team improves the shopper experience, increase advertiser ROI, and improves long-term monetization. This is a talented team of machine learning scientists and software engineers working on complex solutions to understand the customer intent and present them with ads that are not only relevant to their actual shopping experience but also non-obtrusive. This area is of strategic importance to Amazon Retail and Marketplace business, driving long term growth. Key job responsibilities As a Senior Applied Scientist on this team, you will: - Be the technical leader in Machine Learning; lead efforts within this team and across other teams. - Perform hands-on analysis and modeling of enormous data sets 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, 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. - Run A/B 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. - Research new and innovative machine learning approaches. - Recruit Applied Scientists to the team and provide mentorship. About the team Amazon is investing heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate. You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA
Economist, PXT Central Science
US, VA, Arlington
The People eXperience and Technology Central Science Team (PXTCS) uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, wellbeing, and the value of work to Amazonians. We are an interdisciplinary team that combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal. We are looking for economists who are able to apply economic methods to address business problems. The ideal candidate will work with engineers and computer scientists to estimate models and algorithms on large scale data, design pilots and measure their impact, and transform successful prototypes into improved policies and programs at scale. We are looking for creative thinkers who can combine a strong technical economic toolbox with a desire to learn from other disciplines, and who know how to execute and deliver on big ideas as part of an interdisciplinary technical team. Ideal candidates will work in a team setting with individuals from diverse disciplines and backgrounds. They will work with teammates to develop scientific models and conduct the data analysis, modeling, and experimentation that is necessary for estimating and validating models. They will work closely with engineering teams to develop scalable data resources to support rapid insights, and take successful models and findings into production as new products and services. They will be customer-centric and will communicate scientific approaches and findings to business leaders, listening to and incorporate their feedback, and delivering successful scientific solutions. Key job responsibilities Use reduced-form causal analysis and/or structural economic modeling methods to evaluate the impact of policies on employee outcomes, and examine how external labor market and economic conditions impact Amazon's ability to hire and retain talent. A day in the life Work with teammates to apply economic methods to business problems. This might include identifying the appropriate research questions, writing code to implement a DID analysis or estimate a structural model, or writing and presenting a document with findings to business leaders. Our economists also collaborate with partner teams throughout the process, from understanding their challenges, to developing a research agenda that will address those challenges, to help them implement solutions. About the team We are a multidisciplinary team that combines the talents of science and engineering to develop innovative solutions to make Amazon Earth's Best Employer. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA
Actuarial Analyst, Global Risk Management & Claims
US, WA, Seattle
We are expanding our Global Risk Management & Claims team and insurance program support for Amazon’s growing risk portfolio. This role will partner with our risk managers to develop pricing models, determine rate adequacy, build underwriting and claims dashboards, estimate reserves, and provide other analytical support for financially prudent decision making. As a member of the Global Risk Management team, this role will provide actuarial support for Amazon’s worldwide operation. Key job responsibilities ● Collaborate with risk management and claims team to identify insurance gaps, propose solutions, and measure impacts insurance brings to the business ● Develop pricing mechanisms for new and existing insurance programs utilizing actuarial skills and training in innovative ways ● Build actuarial forecasts and analyses for businesses under rapid growth, including trend studies, loss distribution analysis, ILF development, and industry benchmarks ● Design actual vs expected and other metrics dashboards to assist decision makings in pricing analysis ● Create processes to monitor loss cost and trends ● Propose and implement loss prevention initiatives with impact on insurance pricing in mind ● Advise underwriting decisions with analysis on driver risk profile ● Support insurance cost budgeting activities ● Collaborate with external vendors and other internal analytics teams to extract insurance insight ● Conduct other ad hoc pricing analyses and risk modeling as needed We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | New York, NY, USA | Seattle, WA, USA
Senior Applied Scientist
US, NY, New York
The Amazon SCOT Forecasting team seeks a Senior Applied Scientist to join our team. Our research team conducts research into the theory and application of reinforcement learning. This research is shared in top journals and conferences and has a significant impact on the field. Through our launch of several Deep RL models into production, our work also affects decision making in the real world. Members of our group have varied interests—from the mathematical foundations of reinforcement learning, to language modeling, to maintaining the performance of generative models in the face of copyrights, and more. Recent work has focused on sample efficiency of RL algorithms, treatment effect estimation, and RL agents integrating real-world constraints, as applied in supply chains. Previous publications include: - Linear Reinforcement Learning with Ball Structure Action Space - Meta-Analysis of Randomized Experiments with Applications to Heavy-Tailed Response Data - A Few Expert Queries Suffices for Sample-Efficient RL with Resets and Linear Value Approximation - Deep Inventory Management - What are the Statistical Limits of Offline RL with Linear Function Approximation? Working collaboratively with a group of fellow scientists and engineers, you will identify complex problems and develop solutions in the RL space. We encourage collaboration across teammates and their areas of specialty, leading to creative and ambitious projects with the goal of publication and production. Key job responsibilities - Drive collaborative research and creative problem solving - Constructively critique peer research; mentor junior scientists - Create experiments and prototype implementations of new algorithms and techniques - Collaborate with engineering teams to design and implement software built on these new algorithms - Contribute to progress of the Amazon and broader research communities by producing publications We are open to hiring candidates to work out of one of the following locations: New York, NY, USA
Data Scientist, Analytics
US, CA, Virtual Location - California
If you are interested in this position, please apply on Twitch's Career site https://www.twitch.tv/jobs/en/ About Us: Launched in 2011, Twitch is a global community that comes together each day to create multiplayer entertainment: unique, live, unpredictable experiences created by the interactions of millions. We bring the joy of co-op to everything, from casual gaming to world-class esports to anime marathons, music, and art streams. Twitch also hosts TwitchCon, where we bring everyone together to celebrate and grow their personal interests and passions. We're always live at Twitch. About the Role: As a Data Scientist, Analytics member of the Data Platform - Insights team, you'll provide data analysis and support for platform, service, and operational engineering teams at Twitch, shaping the way success is measured. Defining what questions should be asked and scaling analytics methods and tools to support our growing business. Additionally, you will help support the vision for business analytics, solutions architecture for data related business constructs, as well as tactical execution such as experiment analysis and campaign performance reporting. You are paving the way for high-quality, high-velocity decisions and will report to the Manager, Data Science. For this role, we're looking for an experienced data staff who will oversee data instrumentation, dashboard/report building, metrics reviews, inform team investments, guidance on success/failure metrics and ad-hoc analysis. You will also work with technical and non-technical staff members throughout the company, and your effort will have an impact on hundreds of partners at Twitch You Will: - Work with members of Platforms & Services to guide them towards better decision making from the available data. - Promote data knowledge and insights through managing communications with partners and other teams, collaborate with colleagues to complete data projects and ensure all parties can use the insights to further improve. - Maintain a customer-centric focus while being a domain and product expert through data, develop trust amongst peers, and ensure that the teams and programs have access to data to make decisions - Manage ambiguous problems and adapt tools to answer complicated questions. - Identify the trade-offs between speed and quality of different approaches. - Create analytical frameworks to measure team success by partnering with teams to establish success metrics, create approaches to track the data and troubleshoot errors, measure and evaluate the data to develop a common language for all colleagues to understand these metrics. - Operationalize data processes to provide partners with ad-hoc analysis, automated dashboards, and self-service reporting tools so that everyone gets a good sense of the state of the business Perks: - Medical, Dental, Vision & Disability Insurance - 401(k), Maternity & Parental Leave - Flexible PTO - Commuter Benefits - Amazon Employee Discount - Monthly Contribution & Discounts for Wellness Related Activities & Programs (e.g., gym memberships, off-site massages), -Breakfast, Lunch & Dinner Served Daily - Free Snacks & Beverages We are open to hiring candidates to work out of one of the following locations: Irvine, CA, USA | Seattle, WA, USA | Virtual Location - CA
Applied Scientist, Middle Mile Transportation
US, WA, Bellevue
Have you ever ordered a product on Amazon and when that box with the smile arrived you wondered how it got to you so fast? Have you wondered where it came from and how much it cost Amazon to deliver it to you? Have you also wondered what are different ways that the transportation assets can be used to delight the customer even more. If so, the Amazon transportation Services, Product and Science is for you . We manage the delivery of tens of millions of products every week to Amazon’s customers, achieving on-time delivery in a cost-effective manner. We are looking for an enthusiastic, customer obsessed Applied Scientist with strong scientific thinking, good software and statistics experience, skills to help manage projects and operations, improve metrics, and develop scalable processes and tools. The primary role of an Applied Scientist within Amazon is to address business challenges through building a compelling case, and using data to influence change across the organization. This individual will be given responsibility on their first day to own those business challenges and the autonomy to think strategically and make data driven decisions. Decisions and tools made in this role will have significant impact to the customer experience, as it will have a major impact on how we operate the middle mile network. Ideal candidates will be a high potential, strategic and analytic graduate with a PhD in (Operations Research, Statistics, Engineering, and Supply Chain) ready for challenging opportunities in the core of our world class operations space. Great candidates have a history of operations research, machine learning , and the ability to use data and research to make changes. This role requires robust skills in research and implementation of scalable products and models . This individual will need to be able to work with a team, but also be comfortable making decisions independently, in what is often times an ambiguous environment. Responsibilities may include: - Develop input and assumptions based preexisting models to estimate the costs and savings opportunities associated with varying levels of network growth and operations - Creating metrics to measure business performance, identify root causes and trends, and prescribe action plans - Managing multiple projects simultaneously - Working with technology teams and product managers to develop new tools and systems to support the growth of the business - Communicating with and supporting various internal stakeholders and external audiences We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA
Applied Scientist
US, CA, Los Angeles
The Alexa team is looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background, to help build industry-leading Speech and Language technology. Key job responsibilities As an Applied Scientist with the Alexa team, you will work with talented peers to develop novel algorithms and modeling techniques to advance the state of the art in spoken language understanding. 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 spoken language understanding. About the team The Alexa team has a mission to push the envelope in Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), and Audio Signal Processing, in order to provide the best-possible experience for our customers. We are open to hiring candidates to work out of one of the following locations: Los Angeles, CA, USA
View from space of a connected network around planet Earth representing the Internet of Things.
Get more from Amazon Science
Sign up for our newsletter
Subscribe
Amazon.com | Conditions of Use | Privacy | © 1996-2023 Amazon.com, Inc. or its affiliates
Follow Us