Amazon Consumer Science Summit goes virtual

COVID-19-induced trend toward virtual conferences may change how science is conducted.

COVID-19 has caused massive disruption around the globe. That includes the myriad of science conferences held each year. With the pandemic now forcing the cancellation of nearly all in-person events, these conferences have gone virtual.

Amazon is a participant in this trend. In late September, for instance, Amazon’s consumer science organization held its seventh annual Consumer Science Summit, which focused on science applications for economic decision-making, with topics ranging from operations research and econometrics, to statistics and machine learning.  Originally planned as a 200-person conference to be held at a resort in Washington state, event organizers considered how best to proceed. Their final decision: move the summit online.

No one was sure quite what to expect, but the outcome was surprisingly positive. Without the need to limit attendees due to space constraints, and without the need for participants to build travel days into their schedule or worry about acquiring a US visa, attendance was more than four times that of previous in-person events. Moreover, prominent scientists who otherwise might have been unable to attend in person were able to deliver virtual keynote talks.

Ping Xu, forecasting science director, standing outside in front of a rainbow.
Ping Xu, forecasting science director within Amazon's Supply Chain Optimization Technologies (SCOT) organization, was one of the organizers of this year's Consumer Science Summit.

“In this age of isolation and uncertainty, it turns out this event helped us return to the roots of science — the exchange of ideas,” said Ping Xu, forecasting science director within Amazon’s Supply Chain Optimization Technologies organization.

In fact, scientists everywhere are discovering that a well-run virtual event can have benefits that extend beyond an in-person conference.

The Scientist magazine, for instance, reported on an August conference held by the Society for Mathematical Biology and the European Society for Mathematical and Theoretical Biology. Originally set for Heidelberg, Germany, the virtual conference created virtual space so it would provide for socializing, networking, and mentoring, as well as hearing talks, seeing posters, and visiting the meeting’s corporate sponsors.  

Plus, the roughly 1,800 attendees represented more than 90 countries — two to three times as many as at previous in-person meetings. 

The move to virtual scientific conferences poses some intriguing questions. Will it make science more collaborative and multidisciplinary? Or will people lose the chance for serendipitous connections that are the staple of in-person event?

In an effort to make up for the lack of in-person interaction, Consumer Science Summit organizers took advantage of online tools to facilitate collaboration among virtual attendees. The conference had its own Slack channel, for instance, and networking coffee breaks occurred over Chime. The four-day conference included the presentation of 208 papers and abstracts, 5 keynote, 28 lightning, and 12 technical talks, and 2 fireside chats. 

The keynote talks included presentations by Ming Lin, a computer science professor  at the University of Maryland; Anna Nagurney, the John F. Smith Memorial Professor within the Department of Operations and Information Management at the University of Massachusetts Amherst Isenberg School of Management; Susan Athey, an economics of technology professor at Stanford University, and senior fellow of the Stanford Institute for Economic Policy Research; Nassim Taleb, distinguished professor of risk engineering at the NYU Tandon School of Engineering, and author of several books, including “The Black Swan”; and Gerard Cachon, a professor within the Operations, Information and Decisions Department at the University of Pennsylvania Wharton School. The two fireside chats were with Scott Aronson, the David J. Bruton Centennial Professor of Computer Science at the University of Texas at Austin, and Swami Sivasubramanian, vice president, AWS machine learning. Below are three of those keynote talks.

Nassim Taleb: Statistical consequences of fat tails
In this keynote talk, Professor Taleb discusses concepts from the first version of his Technical Incerto: Statistical Consequences of Fat Tails: Real World Preasymptotics, Epistemology, and Applications.
Gerard Cachon: Human vs. robot workers in fulfillment center pick processes
In his keynote talk, Professor Cachon talks about new research he has conducted with Omar Besbes, a professor at Columbia University Business School.
Anna Nagurney: Optimization of food supply chain networks: Why quality, trade instruments, and labor all matter
In her keynote talk, which she dedicated to essential workers, Professor Nagurney discusses her latest research on food supply chain networks.

“It was much better than I expected, given my first exposure to the technology that we would rely upon,” says Robert Stine, senior principal scientist within SCOT. “Some aspects, such as the recorded lightning talks worked very well.  This format was better than the awkward in-person version — and could be combined nicely with online meetings and poster sessions.”

Adds Mauricio Resende, a principal scientist within Amazon’s Transportation Services organization: “I attended the previous four summits, and was pleasantly surprised at how well this virtual conference ran. We had a few glitches where people had to refresh their browsers, but otherwise there were no delays, no problems with the live presentations — or the recorded ones.  And we had a much larger audience.”

Ping Xu, the event’s sponsor, agrees.

“I thought it went really well,” she says. “I was really glad to see that we could draw a wider audience by going virtual. Plus, we learned a lot about how to manage a virtual conference. For instance, one thing I learned is that the virtual format encourages a broader engagement but an in-person format encourages a deeper engagement. For our 2021 conference, we are entertaining a hybrid approach where we can offer online talks for a larger audience but still allow in-person celebrations and discussions.

It was hard at times not to miss the more social aspects of an in-person conference, where scientists can connect with peers they’ve worked with before and perhaps make new connections.

“I still prefer in-person conferences,” says Resende. “They give me a chance to bump into old friends and make new ones. I have begun many a collaboration or paper on the basis of unexpected meetings at conferences.”

In this age of isolation and uncertainty, it turns out this event helped us return to the roots of science – the exchange of ideas
Ping Xu, forecasting science director

Some event organizers are aware of that shortcoming and have developed clever ways to address it.  One conference, for instance, asked participants to write a short description of their work and research interests. That information was then run through an algorithm that matched up attendees with similar interests.   

Prior to the COVID-19 pandemic, there already was a movement within the machine learning science community to allow remote paper and poster presentations at scientific conferences.

Turing Award winner Joshua Bengio is a supporter of the movement allowing more virtual presentations of papers and poster sessions, saying in a blog post that he believes “we should rethink these events with the objective of eliminating the resulting carbon footprint.”

In the post, Bengio suggests the science community could consider decentralized conferences, where instead of having a conference at one location, meeting places could be established — at least one on each continent — so that scientists could attend their “local” meeting.

Some think scientists working remotely more often would have greater impact than having science conferences adopt a completely remote model.

“I think the impact of working virtually reduces carbon footprint more than the impact of going from an in-person conference model to a virtual model,” says Resende. “I have worked remotely two or three days a week since 1988. Virtual conferencing reduces the need to see people face-to-face, but doesn’t eliminate it.”

Other drawbacks include sometimes having too much to choose from, reducing focus. And virtual conference-goers have the same challenge as remote workers everywhere: giving full attention to a conference speaker when emails are coming in and phones are ringing.

Nonetheless, most scientists expect to be attending more virtual conferences in the foreseeable future, if largely because of COVID-19.

On the plus side, a world of virtual conferences will improve as conference organizers grow accustomed to virtual formats, and new conferencing tools become available.  Events are apt to draw more people — as the Amazon summit did — and can take advantage of a wider pool of speakers.

But will that improve science?

“Perhaps,” says Stine. “But there already are lots of ways to exchange ideas virtually, such as ArXiv for sharing manuscripts. And universities now post lectures and seminars on-line. That all might change, of course, with a new generation of scientists who have grown up on virtual campuses.”

No matter if in-person or virtual, the key is the exchange of ideas.  In opening the Consumer Science Summit, Xu, the forecasting science director who helped organize the consumer summit, quoted George Bernard Shaw.

“If you have an apple and I have an apple and we exchange these apples, then you and I will each have an apple. But if you have an idea and I have an idea and we exchange these ideas, then each of us will have two ideas.”

Whether virtual or in-person, the primary purpose of science conferences remains the same: the exchange of ideas as researchers stand on the shoulders of the giants who preceded them, seeking to advance the science.


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Job summaryWould you like to work on a greenfield project that'll help improve the shopping experience of millions of Amazon customers? Want to help invent the next generation technologies in recommender and content optimization systems? We’ve got the perfect job for you.We are a team in a fast-paced organization with a huge impact on hundreds of millions of customers. We innovate at the intersection of customer experience, deep learning, and high-scale machine-learning systems.We are looking for Applied Scientists who love big data, and are capable of inventing and applying Machine Learning, Natural Language Processing, Image processing, Data Mining, Classification and Clustering techniques to solve real world problems and build novel customer facing innovations on Amazon. Our applied scientists work closely with software engineers to put algorithms into practice. They also work in partnership with teams across Amazon to create enormous benefits for our customers.As a member of the our Content Optimization team, you would be expected to move fast, have good judgment on what is and what is not worth exploring, create simple and scalable solutions and identify correct problem sets. You will be surrounded by thought-leaders in the Personalization space who are patent-leaders within Amazon. You will keep the team up-to date with latest academic research in relevant fields.About our team: Our team has the autonomy to decide where we can have the most impact and get down to experimenting. We love metrics and the fast pace. We analyze data to uncover potential opportunities, generate hypotheses, and test them. We refuse to accept constraints, internal or external, and have a strong bias for action. We imagine, build prototypes, validate ideas, and launch follow-up experiments from the successful ones.About our organization: Consider the following problem: every day, millions of customers with unique interests and needs come to Amazon looking for products out of a catalog of over a billion items. Not only do we need to decide what content would be most helpful to customers, we also need to present it in an inspiring manner. The Personalization organization within Amazon is responsible for the secret sauce that not only made Amazon the industry pioneer in building recommender systems at scale, but is also continuing to help raise the bar for building delightful and highly personalized shopping experiences.About you: You are an Applied Scientist with an interest in machine learning, data science, search, or recommendation systems. You have great problem solving skills. You love keeping abreast of the latest technology and use it to help you innovate. You have strong leadership qualities, great judgment, clear communication skills, and a track record of delivering great products. You enjoy working hard, having fun, and making history!
US, CA, Irvine
Job summaryWould you like to work on a greenfield project that'll help improve the shopping experience of millions of Amazon customers? Want to help invent the next generation technologies in recommender and content optimization systems? We’ve got the perfect job for you.We are a team in a fast-paced organization with a huge impact on hundreds of millions of customers. We innovate at the intersection of customer experience, deep learning, and high-scale machine-learning systems.We are looking for Applied Scientists who love big data, and are capable of inventing and applying Machine Learning, Natural Language Processing, Image processing, Data Mining, Classification and Clustering techniques to solve real world problems and build novel customer facing innovations on Amazon. Our applied scientists work closely with software engineers to put algorithms into practice. They also work in partnership with teams across Amazon to create enormous benefits for our customers.As a member of the our Content Optimization team, you would be expected to move fast, have good judgment on what is and what is not worth exploring, create simple and scalable solutions and identify correct problem sets. You will be surrounded by thought-leaders in the Personalization space who are patent-leaders within Amazon. You will keep the team up-to date with latest academic research in relevant fields.About our team: Our team has the autonomy to decide where we can have the most impact and get down to experimenting. We love metrics and the fast pace. We analyze data to uncover potential opportunities, generate hypotheses, and test them. We refuse to accept constraints, internal or external, and have a strong bias for action. We imagine, build prototypes, validate ideas, and launch follow-up experiments from the successful ones.About our organization: Consider the following problem: every day, millions of customers with unique interests and needs come to Amazon looking for products out of a catalog of over a billion items. Not only do we need to decide what content would be most helpful to customers, we also need to present it in an inspiring manner. The Personalization organization within Amazon is responsible for the secret sauce that not only made Amazon the industry pioneer in building recommender systems at scale, but is also continuing to help raise the bar for building delightful and highly personalized shopping experiences.About you: You are an Applied Scientist with an interest in machine learning, data science, search, or recommendation systems. You have great problem solving skills. You love keeping abreast of the latest technology and use it to help you innovate. You have strong leadership qualities, great judgment, clear communication skills, and a track record of delivering great products. You enjoy working hard, having fun, and making history!
US, WA, Seattle
Job summaryAre you excited about using econometrics to make multi-million dollar decisions more Science and Data Driven? Are you interested in supporting Consumer Hardware device concepts from innovative idea inception to launch? Do you want to work on a Economics and Data Science team focused on tackling some of the hardest business questions within the Devices business at Amazon and then scaling those Statistics and Econometrics solutions via internal to Amazon tools? Then this could be the role for you!Amazon.com strives to be Earth's most customer-centric company where people can find and discover anything they want to buy online. We hire the world's brightest minds, offering them a fast paced, technologically sophisticated and friendly work environment. Amazon Devices and Services team is the area of Amazon focused on inventing platforms that delight customers by eliminating friction they have in supplying, entertaining, and managing the home and beyond.The Device Economics team owns demand estimates and pricing recommendations of concept devices before customers know they exist. We support over 100 device-specific analyses a year on hardware and services ranging from Echo Frames to Kindle Paperwhite to Blink Video Camera subscriptions to the Amazon Smart Plug…all prior to launch.. We are a cross-functional Product team working to scale Econometrics through Amazon and beyond by incorporating Science into internal facing tools and making it easier for others to do so as well.In this role, you will support up to senior leadership decision meetings around approving confidential funding requests (PRFAQs) for brand new devices and services, build decisions around how many hardware devices to manufacture prior to receiving any customer signal, and pricing decisions around how to price and promote products and services. You will leverage Science and Tools produced by the Device Economics team such as conjoint demand models to produce these recommendations. As part of the stakeholder-facing arm of the team, you will own relationships with decision makers to help improve the end-customer experience by making the decisions that impact those end-customers more data and Science-driven. In parallel, you will work with Scientists, Economists, Product Managers, and Software Developers to provide meaningful feedback about stakeholder problems to inform business solutions and increase the velocity, quality, and scope behind our recommendations. You will own projects to make progress on Decision Science itself. Through this all, we will invest in your development to pursue your career goals.We are willing to consider L5 candidates across the BA/BIE job families where we'll bar raise your Science skills.
US, WA, Seattle
Job summaryAlexa Smart Home Research is looking for a brilliant quantitative researcher to drive a new program to ensure Alexa always delivers a four star experience. In this role, you will define the roadmap for the SH segmentation program, create experiments to evaluate customer behavior and sentiment that drive these higher quality experiences for our target customers. These insights help Alexa Smart Home Marketing and Product teams make data driven decisions about our marketing and product strategies ensuring products are accurately conveyed, appropriately priced and designed, and with each launch we are moving the needle for customers to help them accomplish their ideal smart home.Key job responsibilities· Identify and propose key opportunities for improving the product development and marketing strategy for Alexa products· Develop and execute research projects, including leading all project phases: methodology and study design, data gathering and manipulation, analysis, interpretation and presentation of results· Lead and execute validation and impact studies· Define project requirements, document business and functional specifications, map current and future state business processes· Build automated mechanisms for evaluating, measuring, and deploying the algorithms and/or models you develop.· Bring a deep level of expertise in one of the Research Marketing disciplines (e.g. Statistics)A day in the lifeAs part of your work, you will lead quantitative research projects that build our understand of smart home customers, identifying what works well and areas of improvement for Alexa Smart Home that will ensure we continue to delight our customers. Excellent business and communication skills are a must to develop and define key business questions and to build data sets that answer those questions. You should be able to work with business customers in understanding the business requirements and research impact.About the teamWe are responsible for UX and market research (foundational, market fit, usability and concept testing), Beta launch readiness and voice of the customer. These services product org-wide customer insights that help SH teams connect directly with customers daily, supporting the end-to-end product readiness, and look around the corner to understand customer and competitor trends.
US, CA, Irvine
Job summaryWould you like to work on a greenfield project that'll help improve the shopping experience of millions of Amazon customers? Want to help invent the next generation technologies in recommender and content optimization systems? We’ve got the perfect job for you.We are a team in a fast-paced organization with a huge impact on hundreds of millions of customers. We innovate at the intersection of customer experience, deep learning, and high-scale machine-learning systems.We are looking for Applied Scientists who love big data, and are capable of inventing and applying Machine Learning, Natural Language Processing, Image processing, Data Mining, Classification and Clustering techniques to solve real world problems and build novel customer facing innovations on Amazon. Our applied scientists work closely with software engineers to put algorithms into practice. They also work in partnership with teams across Amazon to create enormous benefits for our customers.As a member of the our Content Optimization team, you would be expected to move fast, have good judgment on what is and what is not worth exploring, create simple and scalable solutions and identify correct problem sets. You will be surrounded by thought-leaders in the Personalization space who are patent-leaders within Amazon. You will keep the team up-to date with latest academic research in relevant fields.About our team: Our team has the autonomy to decide where we can have the most impact and get down to experimenting. We love metrics and the fast pace. We analyze data to uncover potential opportunities, generate hypotheses, and test them. We refuse to accept constraints, internal or external, and have a strong bias for action. We imagine, build prototypes, validate ideas, and launch follow-up experiments from the successful ones.About our organization: Consider the following problem: every day, millions of customers with unique interests and needs come to Amazon looking for products out of a catalog of over a billion items. Not only do we need to decide what content would be most helpful to customers, we also need to present it in an inspiring manner. The Personalization organization within Amazon is responsible for the secret sauce that not only made Amazon the industry pioneer in building recommender systems at scale, but is also continuing to help raise the bar for building delightful and highly personalized shopping experiences.About you: You are an Applied Scientist with an interest in machine learning, data science, search, or recommendation systems. You have great problem solving skills. You love keeping abreast of the latest technology and use it to help you innovate. You have strong leadership qualities, great judgment, clear communication skills, and a track record of delivering great products. You enjoy working hard, having fun, and making history!
US, WA, Seattle
Job summaryAre you excited about cutting-edge deep-learning NLP, NLU, and Conversational AI? If so, then come and join the Alexa Artificial Intelligence (AI) team. We are the science team behind Amazon’s intelligence voice assistance system and are responsible for the deep learning technology that is central to the automated ranking and arbitration to optimize for end-to-end customer satisfaction.Key job responsibilitiesAs an Applied Scientist, 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.A day in the life· Design, build, test and release predictive ML models· Ensure data quality throughout all stages of acquisition and processing, including such areas as data sourcing/collection, ground truth generation, normalization, and transformation.· Collaborate with colleagues from science, engineering and business backgrounds.· Present proposals and results to partner teams in a clear manner backed by data and coupled with actionable conclusions· Work with engineers to develop efficient data querying and inference infrastructure for both offline and online use casesAbout the teamWe are a science and engineering team part of Alexa AI organization. Our mission is to help Alexa decide which action to take in response to customer requests, incorporating a variety of contextual signals including both direct and indirect customer feedback to provide the best response to the customer. Our work directly contributes to improvement in Alexa business and customer metrics.