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.

Related content

US, WA, Seattle
Do you want to re-invent how millions of people consume video content on their TVs, Tablets and Alexa? We are building a free to watch streaming service called Fire TV Channels (https://techcrunch.com/2023/08/21/amazon-launches-fire-tv-channels-app-400-fast-channels/). Our goal is to provide customers with a delightful and personalized experience for consuming content across News, Sports, Cooking, Gaming, Entertainment, Lifestyle and more. You will work closely with engineering and product stakeholders to realize our ambitious product vision. You will get to work with Generative AI and other state of the art technologies to help build personalization and recommendation solutions from the ground up. You will be in the driver's seat to present customers with content they will love. Using Amazon’s large-scale computing resources, you will ask research questions about customer behavior, build state-of-the-art models to generate recommendations and run these models to enhance the customer experience. You will participate in the Amazon ML community and mentor Applied Scientists and Software Engineers with a strong interest in and knowledge of ML. Your work will directly benefit customers and you will measure the impact using scientific tools.
IN, HR, Gurugram
Our customers have immense faith in our ability to deliver packages timely and as expected. A well planned network seamlessly scales to handle millions of package movements a day. It has monitoring mechanisms that detect failures before they even happen (such as predicting network congestion, operations breakdown), and perform proactive corrective actions. When failures do happen, it has inbuilt redundancies to mitigate impact (such as determine other routes or service providers that can handle the extra load), and avoids relying on single points of failure (service provider, node, or arc). Finally, it is cost optimal, so that customers can be passed the benefit from an efficiently set up network. Amazon Shipping is hiring Applied Scientists to help improve our ability to plan and execute package movements. As an Applied Scientist in Amazon Shipping, you will work on multiple challenging machine learning problems spread across a wide spectrum of business problems. You will build ML models to help our transportation cost auditing platforms effectively audit off-manifest (discrepancies between planned and actual shipping cost). You will build models to improve the quality of financial and planning data by accurately predicting ship cost at a package level. Your models will help forecast the packages required to be pick from shipper warehouses to reduce First Mile shipping cost. Using signals from within the transportation network (such as network load, and velocity of movements derived from package scan events) and outside (such as weather signals), you will build models that predict delivery delay for every package. These models will help improve buyer experience by triggering early corrective actions, and generating proactive customer notifications. Your role will require you to demonstrate Think Big and Invent and Simplify, by refining and translating Transportation domain-related business problems into one or more Machine Learning problems. You will use techniques from a wide array of machine learning paradigms, such as supervised, unsupervised, semi-supervised and reinforcement learning. Your model choices will include, but not be limited to, linear/logistic models, tree based models, deep learning models, ensemble models, and Q-learning models. You will use techniques such as LIME and SHAP to make your models interpretable for your customers. You will employ a family of reusable modelling solutions to ensure that your ML solution scales across multiple regions (such as North America, Europe, Asia) and package movement types (such as small parcel movements and truck movements). You will partner with Applied Scientists and Research Scientists from other teams in US and India working on related business domains. Your models are expected to be of production quality, and will be directly used in production services. You will work as part of a diverse data science and engineering team comprising of other Applied Scientists, Software Development Engineers and Business Intelligence Engineers. You will participate in the Amazon ML community by authoring scientific papers and submitting them to Machine Learning conferences. You will mentor Applied Scientists and Software Development Engineers having a strong interest in ML. You will also be called upon to provide ML consultation outside your team for other problem statements. If you are excited by this charter, come join us!
US, MA, Boston
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Senior Applied Scientist with a strong deep learning background, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As a Senior Applied Scientist with the AGI team, you will work with talented peers to lead the development of novel algorithms and modeling techniques, to advance the state of the art with LLMs. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in generative artificial intelligence (GenAI). About the team The AGI team has a mission to push the envelope in LLMs and multimodal systems, in order to provide the best-possible experience for our customers.
IN, KA, Bengaluru
The Amazon Alexa AI team in India is seeking a talented, self-driven Applied Scientist to work on prototyping, optimizing, and deploying ML algorithms within the realm of Generative AI. Key responsibilities include: - Research, experiment and build Proof Of Concepts advancing the state of the art in AI & ML for GenAI. - Collaborate with cross-functional teams to architect and execute technically rigorous AI projects. - Thrive in dynamic environments, adapting quickly to evolving technical requirements and deadlines. - Engage in effective technical communication (written & spoken) with coordination across teams. - Conduct thorough documentation of algorithms, methodologies, and findings for transparency and reproducibility. - Publish research papers in internal and external venues of repute - Support on-call activities for critical issues Basic Qualifications: - Master’s or PhD in computer science, statistics or a related field - 2-7 years experience in deep learning, machine learning, and data science. - Proficiency in coding and software development, with a strong focus on machine learning frameworks. - Experience in Python, or another language; command line usage; familiarity with Linux and AWS ecosystems. - Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc. - Excellent communication skills (written & spoken) and ability to collaborate effectively in a distributed, cross-functional team setting. - Papers published in AI/ML venues of repute Preferred Qualifications: - Track record of diving into data to discover hidden patterns and conducting error/deviation analysis - Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations - The motivation to achieve results in a fast-paced environment. - Exceptional level of organization and strong attention to detail - Comfortable working in a fast paced, highly collaborative, dynamic work environment
US, WA, Seattle
Have you ever wondered how Amazon launches and maintains a consistent customer experience across hundreds of countries and languages it serves its customers? Are you passionate about data and mathematics, and hope to impact the experience of millions of customers? Are you obsessed with designing simple algorithmic solutions to very challenging problems? If so, we look forward to hearing from you! At Amazon, we strive to be Earth's most customer-centric company, where both internal and external customers can find and discover anything they want in their own language of preference. Our Translations Services (TS) team plays a pivotal role in expanding the reach of our marketplace worldwide and enables thousands of developers and other stakeholders (Product Managers, Program Managers, Linguists) in developing locale specific solutions. Amazon Translations Services (TS) is seeking an Applied Scientist to be based in our Seattle office. As a key member of the Science and Engineering team of TS, this person will be responsible for designing algorithmic solutions based on data and mathematics for translating billions of words annually across 130+ and expanding set of locales. The successful applicant will ensure that there is minimal human touch involved in any language translation and accurate translated text is available to our worldwide customers in a streamlined and optimized manner. With access to vast amounts of data, cutting-edge technology, and a diverse community of talented individuals, you will have the opportunity to make a meaningful impact on the way customers and stakeholders engage with Amazon and our platform worldwide. Together, we will drive innovation, solve complex problems, and shape the future of e-commerce. Key job responsibilities * Apply your expertise in LLM models to design, develop, and implement scalable machine learning solutions that address complex language translation-related challenges in the eCommerce space. * Collaborate with cross-functional teams, including software engineers, data scientists, and product managers, to define project requirements, establish success metrics, and deliver high-quality solutions. * Conduct thorough data analysis to gain insights, identify patterns, and drive actionable recommendations that enhance seller performance and customer experiences across various international marketplaces. * Continuously explore and evaluate state-of-the-art modeling techniques and methodologies to improve the accuracy and efficiency of language translation-related systems. * Communicate complex technical concepts effectively to both technical and non-technical stakeholders, providing clear explanations and guidance on proposed solutions and their potential impact. About the team We are a start-up mindset team. As the long-term technical strategy is still taking shape, there is a lot of opportunity for this fresh Science team to innovate by leveraging Gen AI technoligies to build scalable solutions from scratch. Our Vision: Language will not stand in the way of anyone on earth using Amazon products and services. Our Mission: We are the enablers and guardians of translation for Amazon's customers. We do this by offering hands-off-the-wheel service to all Amazon teams, optimizing translation quality and speed at the lowest cost possible.
GB, London
Are you looking to work at the forefront of Machine Learning and AI? Would you be excited to apply cutting edge Generative AI algorithms to solve real world problems with significant impact? The AWS Industries Team at AWS helps AWS customers implement Generative AI solutions and realize transformational business opportunities for AWS customers in the most strategic industry verticals. This is a team of data scientists, engineers, and architects working step-by-step with customers to build bespoke solutions that harness the power of generative AI. The team helps customers imagine and scope the use cases that will create the greatest value for their businesses, select and train and fine tune the right models, define paths to navigate technical or business challenges, develop proof-of-concepts, and build applications to launch these solutions at scale. The AWS Industries team provides guidance and implements best practices for applying generative AI responsibly and cost efficiently. You will work directly with customers and innovate in a fast-paced organization that contributes to game-changing projects and technologies. You will design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. In this Data Scientist role you will be capable of using GenAI and other techniques to design, evangelize, and implement and scale cutting-edge solutions for never-before-solved problems. Key job responsibilities - Collaborate with AI/ML scientists, engineers, and architects to research, design, develop, and evaluate cutting-edge generative AI algorithms and build ML systems to address real-world challenges - Interact with customers directly to understand the business problem, help and aid them in implementation of generative AI solutions, deliver briefing and deep dive sessions to customers and guide customer on adoption patterns and paths to production - Create and deliver best practice recommendations, tutorials, blog posts, publications, sample code, and presentations adapted to technical, business, and executive stakeholder - Provide customer and market feedback to Product and Engineering teams to help define product direction About the team Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship and Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.
US, WA, Seattle
We’re working on the future. If you are seeking an iterative fast-paced environment where you can drive innovation, apply state-of-the-art technologies to solve large-scale real world delivery challenges, and provide visible benefit to end-users, this is your opportunity. Come work on the Amazon Prime Air Team! We are seeking a highly skilled weather scientist to help invent and develop new models and strategies to support Prime Air’s drone delivery program. In this role, you will develop, build, and implement novel weather solutions using your expertise in atmospheric science, data science, and software development. You will be supported by a team of world class software engineers, systems engineers, and other scientists. Your work will drive cross-functional decision-making through your excellent oral and written communication skills, define system architecture and requirements, enable the scaling of Prime Air’s operation, and produce innovative technological breakthroughs that unlock opportunities to meet our customers' evolving demands. About the team Prime air has ambitious goals to offer its service to an increasing number of customers. Enabling a lot of concurrent flights over many different locations is central to reaching more customers. To this end, the weather team is building algorithms, tools and services for the safe and efficient operation of prime air's autonomous drone fleet.
US, CA, Palo Alto
Amazon Sponsored Products is investing heavily in building a world class advertising business and we are responsible for defining and delivering a collection of GenAI/LLM powered self-service performance advertising products that drive discovery and sales. Our products are strategically important to Amazon’s Selling Partners and key to driving their long-term growth. We deliver billions of ad impressions and clicks daily and are breaking fresh ground to create world-class products. We are highly motivated, collaborative and fun-loving team 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. This role will be pivotal within the Autonomous Campaigns org of Sponsored Products Ads, where we're pioneering the development of AI-powered advertising innovations that will redefine the future of campaign management and optimization. As a Principal Applied Scientist, you will lead the charge in creating the next generation of self-operating, GenAI-driven advertising systems that will set a new standard for the industry. Our team is at the forefront of designing and implementing these transformative technologies, which will leverage advanced Large Language Models (LLMs) and sophisticated chain-of-thought reasoning to achieve true advertising autonomy. Your work will bring to life systems capable of deeply understanding the nuanced context of each product, market trends, and consumer behavior, making intelligent, real-time decisions that surpass human capabilities. By harnessing the power of these future-state GenAI systems, we will develop advertising solutions capable of autonomously selecting optimal keywords, dynamically adjusting bids based on complex market conditions, and optimizing product targeting across various Amazon platforms. Crucially, these systems will continuously analyze performance metrics and implement strategic pivots, all without requiring manual intervention from advertisers, allowing them to focus on their core business while our AI works tirelessly on their behalf. This is not simply about automating existing processes; your work will redefine what's possible in advertising. Our GenAI systems will employ multi-step reasoning, considering a vast array of factors, from seasonality and competitive landscape to macroeconomic trends, to make decisions that far exceed human speed and effectiveness. This autonomous, context-aware approach represents a paradigm shift in how advertising campaigns are conceived, executed, and optimized. As a Principal Applied Scientist, you will be at the forefront of this transformation, tackling complex challenges in natural language processing, reinforcement learning, and causal inference. Your pioneering efforts will directly shape the future of e-commerce advertising, with the potential to influence marketplace dynamics on a global scale. This is an unparalleled opportunity to push the boundaries of what's achievable in AI-driven advertising and leave an indelible mark on the industry. Key job responsibilities • Seek to understand in depth the Sponsored Products offering at Amazon and identify areas of opportunities to grow our business using GenAI, LLM, and ML solutions. • Mentor and guide the applied scientists in our organization and hold us to a high standard of technical rigor and excellence in AI/ML. • Design and lead organization-wide AI/ML roadmaps to help our Amazon shoppers have a delightful shopping experience while creating long term value for our advertisers. • Work with our engineering partners and draw upon your experience to meet latency and other system constraints. • Identify untapped, high-risk technical and scientific directions, and devise new research directions that you will drive to completion and deliver. • Be responsible for communicating our Generative AI/ Traditional AI/ML innovations to the broader internal & external scientific community.
US, CO, Boulder
Do you want to lead the Ads industry and redefine how we measure the effectiveness of the WW Amazon Ads business? Are you passionate about causal inference, Deep Learning/DNN, raising the science bar, and connecting leading-edge science research to Amazon-scale implementation? If so, come join Amazon Ads to be an Applied Science leader within our Advertising Incrementality Measurement science team! Key job responsibilities As an Applied Science leader within the Advertising Incrementality Measurement (AIM) science team, you are responsible for defining and executing on key workstreams within our overall causal measurement science vision. In particular, you will lead the science development of our Deep Neural Net (DNN) ML model, a foundational ML model to understand the impact of individual ad touchpoints for billions of daily ad touchpoints. You will work on a team of Applied Scientists, Economists, and Data Scientists to work backwards from customer needs and translate product ideas into concrete science deliverables. You will be a thought leader for inventing scalable causal measurement solutions that support highly accurate and actionable causal insights--from defining and executing hundreds of thousands of RCTs, to developing an exciting science R&D agenda. You will solve hard problems, advance science at Amazon, and be a leading innovator in the causal measurement of advertising effectiveness. In this role, you will work with a team of applied scientists, economists, engineers, product managers, and UX designers to define and build the future of advertising causal measurement. You will be working with massive data, a dedicated engineering team, and industry-leading partner scientists. Your team’s work will help shape the future of Amazon Advertising.
US, WA, Seattle
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. 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! As a Data Scientist on this team you will: - Lead Data Science solutions from beginning to end. - Deliver with independence on challenging large-scale problems with complexity and ambiguity. - Write code (Python, R, Scala, SQL, etc.) to obtain, manipulate, and analyze data. - Build Machine Learning and statistical models to solve specific business problems. - Retrieve, synthesize, and present critical data in a format that is immediately useful to answering specific questions or improving system performance. - Analyze historical data to identify trends and support optimal decision making. - Apply statistical and machine learning knowledge to specific business problems and data. - Formalize assumptions about how our systems should work, create statistical definitions of outliers, and develop methods to systematically identify outliers. Work out why such examples are outliers and define if any actions needed. - Given anecdotes about anomalies or generate automatic scripts to define anomalies, deep dive to explain why they happen, and identify fixes. - Build decision-making models and propose effective solutions for the business problems you define. - Conduct written and verbal presentations to share insights to audiences of varying levels of technical sophistication. Why you will love this opportunity: Amazon has invested 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. Impact and Career Growth: 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. Team video ~ https://youtu.be/zD_6Lzw8raE