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Long Beach, California
Conference
KDD 2023
August 6 - 10, 2023
Long Beach, California

Overview

The Knowledge Discovery and Data Mining Conference brings together researchers and practitioners from data science, data mining, knowledge discovery, large-scale data analytics, and big data.

Sponsorship Details

Platinum
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Booth 101 & 102

Amazon organizing committee members

Accepted publications

Workshops

KDD 2023 International Workshop on Multimodal Learning
August 7
The recent advancements in machine learning and artificial intelligence (particularly foundation models such as BERT, GPT-3, T5, ResNet, etc.) have demonstrated remarkable capabilities and driven significant revolutionary changes to the way we make inferences from complex data. These models represent a fundamental shift in the way data are approached and offer exciting new research directions and opportunities for multimodal learning and data fusion.

Amazon organizers: George Karypis, Chandan K. Reddy, Yuan Ling, Fanyyou Wu, Shujing Dong, and Yarong Feng

Website: https://sites.google.com/view/multimodal-kdd-2023/
KDD 2023 International Workshop on Mining and Learning from Time Series (MileTS)
August 7
Dynamic ensemble for probabilistic time-series forecasting via deep reinforcement learning
Yuhao Ding, Youngsuk Park, Karthick Gopalswamy, Hilaf Hasson, Bernie Wang, Luke Huan
2023
Machine learning
Deep sequence modeling for event log-based predictive maintenance
Yun Zhou, Yawei Wang, Huan Song, Tesfagabir Meharizghi, Mohamad Al Jazaery, Panpan Xu, Denisse Colin-Magana, Aruna Abeyakoon
2023
Machine learning
Distribution-free multi-horizon forecasting and vending system
Vincent Quenneville-Belair, Malcolm Wolff, Brady Willhelme, Dhruv Madeka, Dean Foster
2023
Machine learning
KDD 2023 ACM SIGKDD Workshop on Causal Discovery, Prediction and Decision (CDPD)
August 7
Leveraging covariate adjustments at scale in online A/B testing
Lorenzo Masoero, Doug Hains, James McQueen
2023
Economics
KDD 2023 Workshop on e-Commerce and NLP (ECNLP 6)
August 7 - August 8
TAAL: Target-aware active learning
Kunal Kotian, Indranil Bhattacharya, Shikhar Gupta, Kaushik Pavani, Naval Bhandari, Sunny Dasgupta
2023
Machine learning
KDD 2023 Workshop on Artificial Intelligence for Computational Advertising (AdKDD)
August 7
Scaling generative pre-training for user ad activity sequences
Sharad Chitlangia, Krishna Reddy Kesari, Rajat Agarwal
2023
Machine learning
Advancing ad auction realism: Practical insights & modeling implications
Ming Chen, Sareh Nabi, Marciano Siniscalchi
2023
Economics
KDD 2023 Workshop on Robust NLP for Finance (RobustFin)
August 7
Cash transaction booking via retrieval augmented LLM
Sabrina zhang, Daksha Yadav, Tom Jin
2023
Conversational AI / Natural-language processing
KDD 2023 Workshop on Machine Learning in Finance (MLF)
August 6
Transformer based anomaly detection on multivariate time series subledger data
Daksha Yadav, Sabrina zhang, Tom Jin
2023
Machine learning
KDD 2023 Workshop on Artificial Intelligence-Enabled Cybersecurity Analytics
August 6
Detecting robotic and compromised IPs in digital advertising
Rohit R R, Rakshith V, Agniva Som
2023
Machine learning
Learning explainable network request signatures for robot detection
Rajat Agarwal, Sharad Chitlangia, Anand Muralidhar, Adithya Niranjan, Abheesht Sharma, Koustav Sadhukhan, Suraj Satishkumar Sheth
2023
Machine learning
KDD 2023 Workshop on Online and Adaptive Recommender Systems (OARS)
August 6
Decision layer: Enhancing multi-model, multi timescale decisions on the fly with online feedback
Meet Gandhi, Agniva Som, Suraj Satishkumar Sheth, Amrita Kumari
2023
Machine learning
KDD 2023 Workshop on End-End Customer Journey Optimization
August 6
Teacher-student learning on complexity in intelligent routing
Shu-Ting Pi, Michael Yang, Yuying Zhu, Qun Liu
2023
Conversational AI / Natural-language processing
KDD 2023 Workshop on Decision Intelligence and Analytics for Online Marketplaces
August 6
Contact complexity in customer service
Shu-Ting Pi, Michael Yang, Qun Liu
2023
Conversational AI / Natural-language processing
Uncovering customer issues through topological natural language analysis
Shu-Ting Pi, Sidarth Srinivasan, Yuying Zhu, Michael Yang, Qun Liu
2023
Conversational AI / Natural-language processing
KDD 2023 Workshop on Mining and Learning with Graphs
August 6
The workshop serves as a forum for researchers from a variety of fields working on mining and learning from graphs to share and discuss their latest findings.

Amazon organizers: Shobeir Fakhraei and Da Zheng

Website: http://www.mlgworkshop.org/2023/
A heterogeneous graph-based framework for scalable fraud detection
Phanindra Reddy Madduru, Naveed Janvekar
2023
Machine learning
Graph model explainer tool
Yudi Zhang, Naveed Janvekar, Phanindra Reddy Madduru, Nitika Bhaskar
2023
Machine learning
Computation of node distances on hypergraphs
Enzhi Li, Bilal Fadlallah
2023
Information and knowledge management
Multi-task learning on heterogeneous graph neural network for substitute recommendation
Tianchen Zhou, Michinari Momma, Chaosheng Dong, Fan Yang, Chenghuan Guo, Jin Shang, Jia (Kevin) Liu
2023
Machine learning
Topological representation learning for e-commerce shopping behaviors
Yankai Chen, Tuan Truong, Xin Shen, Ming Wang, Jin Li, Jim Chan, Irwin King
2023
Machine learning
KDD 2023 Workshop on Multi-Armed Bandits and Reinforcement Learning (MARBLE)
August 7
MESOB: Balancing equilibria & social optimality
Xin Guo, Lihong Li, Sareh Nabi, Rabih Salhab, Junzi Zhang
2023
Economics
KDD 2023 Workshop on Causal Inference and Machine Learning in Practice: Use cases for Product, Brand, Policy and Beyond
August 7
OpportunityFinder: A framework for automated causal inference
Huy Nguyen, Prince Grover, Devashish Khatwani
2023
Economics
KDD 2023 FL4Data-Mining Workshop
August 8
Once-for-All Federated Learning: Learning from and deploying to heterogeneous clients
Kamala Varma, Enmao Diao, Tanya Roosta, Jie Ding, Tao Zhang
2023
Cloud and systems

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Applied Scientist
GB, Cambridge
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Intern - Economics, PXT Central Science
US, WA, Seattle
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Senior Applied Scientist, Sponsored Products
US, NY, New York
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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
Senior Economist, Amazon Advertising Impact Team
US, WA, Seattle
Amazon Advertising Impact Team is looking for a Senior Economist to help translate cutting-edge causal inference and machine learning research into production solutions. The individual will have the opportunity to shape the technical and strategic vision of a highly ambiguous problem space, and deliver measurable business impacts via cross-team and cross-functional collaboration. Amazon is investing heavily in building a world class advertising business. Our advertising products are strategically important to Amazon’s Retail and Marketplace businesses for driving long-term growth. The mission of the Advertising Impact Team is to make our advertising products the most customer-centric in the world. We specialize in measuring and modeling the short- and long-term customer behavior in relation to advertising, using state of the art econometrics and machine learning techniques. With a broad mandate to experiment and innovate, we are constantly advancing our experimentation methodology and infrastructure to accelerate learning and scale impacts. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and bias for action. Key job responsibilities • Function as a technical leader to shape the strategic vision and the science roadmap of a highly ambiguous problem space • Develop economic theory and deliver econometrics and machine learning models to optimize advertising strategies on behalf of our customers • Design, execute, and analyze experiments to verify the efficacy of different scientific solutions in production • Partner with cross-team technical contributors (scientists, software engineers, product managers) to implement the solution in production • Write effective business narratives and scientific papers to communicate to both business and technical audience, including the most senior leaders of the company We are open to hiring candidates to work out of one of the following locations: New York, NY, USA | Seattle, WA, 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
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
Principal Applied Scientist, Global Talent Management & Compensation
US, WA, Seattle
Interested in using the latest, cutting edge machine learning and science to improve the Amazon employee experience? This role provides applied science leadership to the organization that develops and delivers data-driven insights, personalization, and nudges into Amazon's suite of talent management products to help managers, employees, and organizational leaders make better decisions and have better, more equitable outcomes. Key job responsibilities As the Principal Applied Scientist for GTMC SIERRA, you will be responsible for providing scientific thought leadership over multiple applied science and engineering teams. Each of these teams has rapidly evolving and complex demands to define, develop, and deliver scalable products that make work easier, more efficient, and more rewarding for Amazonians. These are some of Amazon’s most strategic technical investments in the people space and support Amazon’s efforts to be Earth’s Best Employer. In this role you will have a significant impact on 1.5 million Amazonians and the communities Amazon serves. You will also play a critical role in the organization's business planning, work closely with senior executives to develop goals and resource requirements, influence our long-term technical and business strategy, and help hire and develop engineering and science talent. You will provide science thought leadership and support to business partners, helping them use the best scientific methods and science-driven tools to solve current and upcoming challenges and deliver efficiency gains in a changing market. About the team Global Talent Management & Compensation (GTMC) SIERRA (Science, Insights, Experience, Research, Reporting & Analytics) is a horizontal, multi-disciplinary organization whose mission is to be a force multiplier for the broader GTMC organization and our key customer cohorts. We accomplish this by using our expertise in data analytics and science, economics, machine learning (ML), UX, I/O psychology, and engineering to build insights and experiences that raise the bar in understanding and shaping decision making at scale by integrating within and across talent journeys as well as through self-service tools and closed loop mechanisms outside of those journeys. Our portfolio of products spans foundational data sources, metrics, and research through to finished features and products that our end-customers interact with on a daily basis. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
Economist, Recruiting Engine
US, WA, Seattle
The economics team within Recruiting Engine uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, well-being, and the value of work to Amazonians. We are an interdisciplinary team, which uses a range of approaches to develop and deliver solutions that measurably achieve this goal. We are looking for an Economist who is able to provide structure around complex business problems, hone those complex problems into specific, scientific questions, and test those questions to generate insights. The ideal candidate will work with various science, engineering, operations and analytics teams 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. She/He/They will produce robust, objective research results and insights which can be communicated to a broad audience inside and outside of Amazon. Ideal candidates will work closely with business partners to develop science that solves the most important business challenges. She/He/They will work well in a team setting with individuals from diverse disciplines and backgrounds. She/He/They will serve as an ambassador for science and a scientific resource for business teams. Ideal candidates will own the development of scientific models and manage the data analysis, modeling, and experimentation that is necessary for estimating and validating the model. They will be customer-centric – clearly communicating scientific approaches and findings to business leaders, listening to and incorporate their feedback, and delivering successful scientific solutions. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Seattle, WA, USA
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