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Developing algorithms and statistical models that computer systems use to perform tasks without explicit instructions, relying on patterns and inference instead.
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  • Efficient switchback experiments via multiple randomization designs
    Lorenzo Masoero, Guido Imbens, Thomas Richardson, James McQueen, Suhas Vijaykumar, Ido Rosen
    2023 Conference on Digital Experimentation @ MIT (CODE@MIT)
    2023
    Online A/B tests have become an indispensable tool across all the technology industry: if performed correctly, “online” experiments can inform effective decision making and product development. It should therefore not be surprising that Gupta et al. [2019] estimates that online businesses alone collectively run hundreds of thousands of experiments annually. Modern online experiments are often run in marketplaces
    Economics
  • Deep layers beware: Unraveling the surprising benefits of JPEG compression for image classification pre-processing
    Guru Nayak, Gerald Friedland
    IEEE ISM 2023
    2023
    In this paper, we explore the intriguing effects of JPEG compression as a pre-processing technique for image classification tasks. Building upon the findings of a previous study by Friedland et al., which demonstrated that substantial JPEG compression does not significantly degrade classification accuracy, we investigate the potential benefits and limitations of this approach when applied to various classifiers
    Computer vision
  • NICE: CVPR 2023 challenge on zero-shot image captioning
    Taehoon Kim, Pyunghwan Ahn, Sangyun Kim, Sihaeng Lee, Mark Marsden, Alessandra Sala, Seung Hwan Kim, Bohyung Han, Kyoung Mu Lee, Honglak Lee, Kyounghoon Bae, Xiangyu Wu, Yi Gao, Hailiang Zhang, Yang Yang, Weili Guo, Jianfeng Lu, Youngtaek Oh, Jae Won Cho, Dong-Jin Kim, In So Kweon, Junmo Kim, Wooyoung Kang, Won Young Jhoo, Byungseok Roh, Jonghwan Mun, Solgil Oh, Kenan Emir Ak, Gwang Gook Lee, Yan Xu, Mingwei Shen, Kyomin Hwang, Wonsik Shin, Kamin Lee, Wonhark Park, Dongkwan Lee, Nojun Kwak, Yujin Wang, Yimu Wang, Tiancheng Gu, Xingchang Lv, Mingmao Sun
    CVPR 2023 Workshop on New Frontiers for Zero-Shot Image Captioning Evaluation
    2023
    In this report, we introduce NICE (New frontiers for zero-shot Image Captioning Evaluation) project1 and share the results and outcomes of 2023 challenge. This project is designed to challenge the computer vision community to develop robust image captioning models that advance the state-of-the-art both in terms of accuracy and fairness. Through the challenge, the image captioning models were tested using
    Computer vision
  • How do multimodal LLMs really fare in classical vision few-shot challenges? A deep dive
    Qing Guo, Prashan Wanigasekara, Skyler Zheng, Jacob Zhiyuan Fang, Xinwei Deng, Chenyang Tao
    NeurIPS 2023 Workshop on Robustness of Zero/Few-shot Learning in Foundation Models (R0-FoMo)
    2023
    Recent advances in multimodal foundational models have demonstrated marvelous in-context learning capabilities for diverse vision-language tasks. However, existing literature have mainly focused on few-shot learning tasks similar to their NLP counterparts. It is unclear whether these foundation models can also address classical vision challenges such as few-shot classification, which in some settings (e.g
    Machine learning
  • Finite-time logarithmic Bayes regret upper bounds
    Alexia Atsidakou, Branislav Kveton, Sumeet Katariya, Constantine Caramanis, Sujay Sanghavi
    NeurIPS 2023
    2023
    We derive the first finite-time logarithmic Bayes regret upper bounds for Bayesian bandits. In Gaussian bandits, we obtain O(cΔ log n) and O(ch log2n) bounds for an upper confidence bound algorithm, where ch and cΔ are constants depending on the prior distribution and the gaps of random bandit instances sampled from it, respectively. The latter bound asymptotically matches the lower bound of Lai (1987).
    Machine learning
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Data Scientist , ADSP: Campaign & Creative
GB, London
How can Amazon improve the advertising experience for customers around the world? How can we help advertisers and customers find each other in a meaningful way? Amazon Advertising creates and transforms the connection between retailers/service providers and customers. Our teams strive to reinvent the way advertisers and agencies build brands and drive performance in their advertising. By using Amazon's foundation in e-commerce, we help brands connect with the right customers through creative solutions and formats across screens and devices, and in the physical world. Amazon Advertising seeks a Data Scientist with strong Data Analysis skills to join the ADSP engineering team split across Edinburgh and London. We make Guidance products that help optimise our customer's advertising campaign workflows and performance. As a scientist on the team, you will be involved in many aspects of the process - from idea generation, business analysis and scientific research, through to development - giving you a real sense of ownership. The systems that you help to build will operate at massive scale to advertising customers around the world. Our ideal candidate is an experienced Data scientist who has a track-record of performing analysis, applying statistical techniques and building basic ML models to solve real business problems, who has great leadership and communication skills, and who is motivated to achieve results in a fast-paced environment. Key job responsibilities Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative analysis and business judgment. Collaborate with software engineering teams to integrate successful experimental results into large-scale, highly complex Amazon production systems. Report results in a manner which is both statistically rigorous and compellingly relevant, exemplifying good scientific practice in a business environment. Promote the culture of experimentation at Amazon.
Applied Scientist
US, NY, New York
Amazon is looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help build industry-leading language technology. AWS Utility Computing (UC) provides product innovations — from foundational services such as Amazon’s Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), to consistently released new product innovations that continue to set AWS’s services and features apart in the industry. As a member of the UC organization, you’ll support the development and management of Compute, Database, Storage, Internet of Things (Iot), Platform, and Productivity Apps services in AWS, including support for customers who require specialized security solutions for their cloud services. Our mission is to provide a delightful experience to Amazon’s customers by pushing the envelope in Natural Language Processing (NLP), Generative AI, Large Language Model (LLM), Natural Language Understanding (NLU), Machine Learning (ML), Retrieval-Augmented Generation, Responsible AI, Agent, Evaluation, and Model Adaptation. As part of our AI team in Amazon AWS, you will work alongside internationally recognized experts to develop novel algorithms and modeling techniques to advance the state-of-the-art in human language technology. Your work will directly impact millions of our customers in the form of products and services, as well as contributing to the wider research community. You will gain hands on experience with Amazon’s heterogeneous text and structured data sources, and large-scale computing resources to accelerate advances in language understanding. The Science team at AWS Bedrock builds science foundations of Bedrock, which is a fully managed service that makes high-performing foundation models available for use through a unified API. We are adamant about continuously learning state-of-the-art NLP/ML/LLM technology and exploring creative ways to delight our customers. In our daily job we are exposed to large scale NLP needs and we apply rigorous research methods to respond to them with efficient and scalable innovative solutions. At AWS Bedrock, you’ll experience the benefits of working in a dynamic, entrepreneurial environment, while leveraging AWS resources, one of the world’s leading cloud companies and you’ll be able to publish your work in top tier conferences and journals. We are building a brand new team to help develop a new NLP service for AWS. You will have the opportunity to conduct novel research and influence the science roadmap and direction of the team. Come join this greenfield opportunity! About the team Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the 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. 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 & 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. 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.
Applied Scientist AGI AfroTech, Artificial General Intelligence
US, CA, Sunnyvale
The Artificial General Intelligence (AGI) team is looking for a highly skilled and experienced Senior Applied Scientist, to lead the development and implementation of cutting-edge algorithms and models for supervised fine-tuning and reinforcement learning through human feedback; with a focus across text, image, and video modalities. As a Senior Applied Scientist, you will play a critical role in driving the development of Generative AI (GenAI) technologies that can handle Amazon-scale use cases and have a significant impact on our customers' experiences. Key job responsibilities - Collaborate with cross-functional teams of engineers, product managers, and scientists to identify and solve complex problems in GenAI - Design and execute experiments to evaluate the performance of different algorithms and models, and iterate quickly to improve results - Think big about the arc of development of GenAI over a multi-year horizon, and identify new opportunities to apply these technologies to solve real-world problems - Communicate results and insights to both technical and non-technical audiences, including through presentations and written reports - Mentor and guide junior scientists and engineers, and contribute to the overall growth and development of the team
Data Scientist, Consumables (消費財事業部)
JP, 13, Tokyo
Amazon Japan is seeking an experienced Sr. Data Scientist to join our growing team. In this critical role, you will leverage your strong quantitative and analytical skills to drive data-driven insights that shape our FMCG (fast-moving consumer goods) business and other key strategic initiatives. Your responsibilities will include: - Solving complex, ambiguous business problems using appropriate statistical methodologies, modeling techniques, and data science best practices to lead business insights for FMCG business growth. You will work closely with cross-functional partners to translate business requirements into actionable data science solutions. - Designing and implementing scalable, reliable, and efficient data pipelines to extract valuable insights from diverse data sources. This includes making appropriate trade-offs between short-term and long-term needs. - Communicating your findings and recommendations clearly and persuasively to technical and non-technical stakeholders. You will document your work to the highest standards and ensure your solutions have a measurable impact on the business. - Mentoring and developing more junior data scientists on your team. You will actively participate in the hiring process and contribute to the growth of Amazon's data science community. - Staying abreast of the latest advancements in data science and applying innovative techniques where appropriate to tackle challenging business problems.
Applied Scientist, Amazon
US, WA, Seattle
We are seeking a talented applied researcher to join the Whole Page Planning and Optimization (WPPO) Science team in Search. The latest data from Business Insider shows that almost 50% of online shoppers visit Amazon first. The Search WPPO Science team is responsible for developing reinforcement learning systems for the next generation Amazon shopping experience and delivering it to millions of customers. We believe that shopping on Amazon should be simple, delightful, and full of WOW moments for EVERYONE, whether you are technically savvy or new to online shopping. As an Applied Scientist, you will be working closely with a team of applied scientists and engineers to build systems that shape the future of Amazon's shopping experience by automatically generating relevant content and building a whole page experience that is coherent, dynamic, and interesting. You will improve ranking and optimization in our algorithm. You will participate in driving features from idea to deployment, and your work will directly impact millions of customers. You are going to love this job because you will: * Apply state-of-the-art Machine Learning (ML) algorithms, including Deep Learning and Reinforcement Learning, to improve hundreds of millions of customers’ shopping experience. * Have measurable business impact using A/B testing. * Work in a dynamic team that provides continuous opportunities for learning and growth. * Work with leaders in the field of machine learning.
Applied Scientist, Conversational AI ModEling and Learning
US, WA, Bellevue
Conversational AI ModEling and Learning (CAMEL) team is part of Amazon Devices organization where our mission is to build a best-in-class Conversational AI that is intuitive, intelligent, and responsive, by developing superior Large Language Models (LLM) solutions and services which increase the capabilities built into the model and which enable utilizing thousands of APIs and external knowledge sources to provide the best experience for each request across millions of customers and endpoints. We are looking for a passionate, talented, and resourceful Applied Scientist in the field of LLM, Artificial Intelligence (AI), Natural Language Processing (NLP), Recommender Systems and/or Information Retrieval, to invent and build scalable solutions for a state-of-the-art context-aware conversational AI. A successful candidate will have strong machine learning background and a desire to push the envelope in one or more of the above areas. The ideal candidate would also have hands-on experiences in building Generative AI solutions with LLMs, enjoy operating in dynamic environments, be self-motivated to take on challenging problems to deliver big customer impact, moving fast to ship solutions and then iterating on user feedback and interactions. Key job responsibilities As an Applied Scientist, you will leverage your technical expertise and experience to collaborate with other talented applied scientists and engineers to research and develop novel algorithms and modeling techniques to reduce friction and enable natural and contextual conversations. You will analyze, understand and improve user experiences by leveraging Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in artificial intelligence. You will work on core LLM technologies, including Prompt Engineering and Optimization, Supervised Fine-Tuning, Learning from Human Feedback, Evaluation, Self-Learning, etc. Your work will directly impact our customers in the form of novel products and services.
Applied Scientist, Conversational AI ModEling and Learning
US, WA, Bellevue
Conversational AI ModEling and Learning (CAMEL) team is part of Amazon Artificial General Intelligence (AGI) organization where our mission is to create a best-in-class Conversational AI that is intuitive, intelligent, and responsive, by developing superior Large Language Models (LLM) solutions and services which increase the capabilities built into the model and which enable utilizing thousands of APIs and external knowledge sources to provide the best experience for each request across millions of customers and endpoints. We are looking for a passionate, talented, and resourceful Applied Scientist in the field of LLM, Artificial Intelligence (AI), Natural Language Processing (NLP), Recommender Systems and/or Information Retrieval, to invent and build scalable solutions for a state-of-the-art context-aware conversational AI. A successful candidate will have strong machine learning background and a desire to push the envelope in one or more of the above areas. The ideal candidate would also have hands-on experiences in building Generative AI solutions with LLMs, enjoy operating in dynamic environments, be self-motivated to take on challenging problems to deliver big customer impact, moving fast to ship solutions and then iterating on user feedback and interactions. Key job responsibilities As an Applied Scientist, you will leverage your technical expertise and experience to collaborate with other talented applied scientists and engineers to research and develop novel algorithms and modeling techniques to reduce friction and enable natural and contextual conversations. You will analyze, understand and improve user experiences by leveraging Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in artificial intelligence. You will work on core LLM technologies, including Supervised Fine-Tuning (SFT), In-Context Learning (ICL), Learning from Human Feedback (LHF), etc. Your work will directly impact our customers in the form of novel products and services.
Applied Scientist , Gen AI
IL, Tel Aviv
Are you an inventive, curious, and driven Applied Scientist with a strong background in AI and Deep Learning? Join Amazon’s AWS Multimodal generative AI science team and be a catalyst for groundbreaking advancements in Computer Vision, Generative AI, and foundational models. As part of the AWS Multimodal generative AI science team, you’ll lead innovative research projects, develop state-of-the-art algorithms, and pioneer solutions that will directly impact millions of Amazon customers. Leveraging Amazon’s vast computing power, you’ll work alongside a supportive and diverse group of top-tier scientists and engineers, contributing to products that redefine the industry. Key job responsibilities * Lead research initiatives in Multimodal generative AI, pushing the boundaries of model efficiency, accuracy, and scalability. * Design, implement, and evaluate deep learning models in a production environment. * Collaborate with cross-functional teams to transfer research outcomes into scalable AWS services. * Publish in top-tier conferences and journals, keeping Amazon at the forefront of innovation. * Mentor and guide other scientists and engineers, fostering a culture of scientific curiosity and excellence.
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 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. - 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. - 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
Economist, Amazon Customer Service
US, WA, Seattle
We are seeking a highly skilled economist to measure and understand how each Customer Service activity impacts customers. This candidate's analysis will assist teams across Amazon to prioritize defect elimination efforts and optimize how we respond to customer contacts. This candidate will partner closely with our product, program, and tech teams to deliver their findings to users via systems and dashboards that guide Customer Service planning and policy rules. Key job responsibilities - Develop Causal, Economic, and Machine Learning models at scale. - Engage in economic analysis and raise the bar for research. - Inform strategic discussions with senior leaders across the company to guide policies. A day in the life If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply! Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan About the team The Worldwide defect elimination team's mission is to understand and resolve all issues impacting customers at scale. The Customer Service Economics and Optimization team is a force multiplier within this group, helping to understand the impact of these issues and our actions to optimize the customer experience.
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