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Credit: Glynis Condon

Seven Amazon scientists shaping the future of AI

To commemorate International Women’s Day, we spoke to women scientists across a variety of research areas at Amazon.

To commemorate International Women’s Day (IWD), during Women's History Month, we asked scientists across a variety of Amazon research areas about their backgrounds, and the most exciting innovations in their fields. Here’s what they had to say.

Xin Luna Dong, principal scientist

Xin Luna Dong
Xin Luna Dong, principal scientist

Dong is a principal scientist, leading the efforts to develop the Amazon Product Knowledge Graph. Dong received her PhD in computer science, with a focus on data integration, from the University of Washington. The personal information management system in Dong’s dissertation (which won the Best Demo award in Sigmod’ 2005), is a personal knowledge graph developed at least five years before the phrase “knowledge graph” was coined. After graduation, Dong led the development of the Knowledge-based Trust project at Google. Dong has co-authored the book Big Data Integration. She is an ACM Distinguished Member, and has received the VLDB Early Career Research Contribution Award for "advancing the state of the art of knowledge fusion”. Dong was program committee co-chair for Sigmoid 2018, and is program committee co-chair for VLDB 2020. She also serves on the VLDB endowment and PVLDB advisory committees.

Innovations I find exciting

A recent innovation that I’m most excited about is graph neural network (GNN). Unlike recurrent neural networks (RNNs) and convolutional neural networks (CNNs), which focus much more on regular data such as word sequences, 2-D images, and 3-D videos, GNNs allow us to leverage graphs to capture much more complex relationships. These include elements in the graph, represented by nodes in the graph, and their relationships, represented by edges between the nodes. Examples of graphs influenced by GNNs include social networks, world wide web (WWW) topology, knowledge graphs, and molecular graphs. As we build knowledge graphs for products, it is amazing how many different ways we can benefit from GNNs.

Naturally, we can apply GNNs on the knowledge graphs we have built, to discern interesting patterns to find popular artists in the music domain. We also model webpage layouts as graphs, and model customer behaviors as graphs, so GNNs help us extract relevant knowledge and enrich our knowledge graphs. This new technique enables us to be so much more creative in the practice of constructing knowledge graphs, and applying the findings to real-world applications.

Claire Law, senior technical program manager

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Claire Law, senior technical program manager

Law is senior technical program manager on Amazon’s physical retail team, the team behind the Just Walk Out technology used in Amazon Go and Amazon Go Grocery stores. She studied nanotechnology engineering at the University of Waterloo. Early on, she realized that she didn’t enjoy the type of lab work expected from a researcher in material science. She leveraged a university program, and interned as a software developer, marketer, hardware test engineer, project control officer in consulting, and software program manager. These experiences, coupled with work experiences at Microsoft and Research in Motion, led Law to pursue a career in software.

After a stint in Amazon’s international organization, Law joined the physical retail team to work on machine vision initiatives. On this team, Law is able to leverage her experience in cloud computing and knowledge of optics and photography to build new experiences for physical retail.

Innovations I find exciting

We are only now reaching a level where computer vision can solve real-world problems in a meaningful way. While we still need to be creative in where we look for simplifiers, algorithms are able to solve more and more problems every day. Challenges that looked insurmountable just a couple years ago are now part of production systems across the industry. Checkout-free stores seemed like science fiction before Amazon Go was launched, and now customers are loving this effortless shopping experience in the 25 Amazon Go stores, and the new Amazon Go Grocery store we have open today.

Yoelle Maarek, vice president

Yoelle Maarek, vice president of research and science for Alexa Shopping
Yoelle Maarek, vice president of research and science for Alexa Shopping

Maarek is vice president of research and science, Alexa Shopping. Prior to Amazon, Maarek served in engineering and research leadership roles at Yahoo, Google and IBM. Maarek has been regularly serving as program committee (PC) chair and senior PC committee member at leading academic research conferences related to Web search and data mining, such as SIGIR, The Web Conference, and Web Search and Data Mining (WSDM). She is currently serving on the steering committees of WSDM and the Web Conference series.

She is a member of the Technion Board of Governors and was inducted as an ACM Fellow in 2013. Maarek obtained a PhD in computer science from the Technion, Israel in 1989. She holds an engineering degree from the Ecole des Ponts et Chaussées, and a DEA (graduate degree) in computer science from Paris VI university. Maarek completed her PhD at the Technion in Israel and was a visiting student at Columbia University. She played a pioneering role within industry in researching the field of information retrieval, the computer science discipline behind search, in the pre-Web era, and led the launch of Google Suggest, the query auto-completion capability. As such, she jokingly refers to herself as a “search dinosaur”.

Innovations I find exciting

We are on the verge of making ambient computing happen, and Alexa is pioneering this long-awaited revolution. It forces us to revisit all our assumptions across multiple domains. I see this prevalent especially in search and question answering. These are topics close to my heart. I have been following progress in these areas since I got my PhD thirty years ago. The focus on ambient computing is also a unique opportunity for us at Amazon to demonstrate what we mean by customer-obsessed science. As humans are learning to interact with machines, their behavior is evolving and we need to follow suit. It not only challenges scientists to keep inventing on behalf of customers but also forces all of us to remain humble. We are not here to teach customers how to speak to a machine, but rather to do everything in our power to understand, satisfy and predict their needs so as to constantly wow and delight them.

Angeliki Metallinou, applied science manager

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Angeliki Metallinou, applied science manager

Metallinou is an applied science manager within the Amazon Alexa AI Natural Understanding group. She received both her PhD, and master’s degree in electrical engineering from the University of Southern California. Her interests and experience lie in the areas of spoken and natural language understanding, dialogue systems, machine learning, deep learning, affective computing and applications for education and healthcare.

She has published papers in the areas of speech, language, dialogue, artificial intelligence and multimodal human computer interaction at leading science conferences such as Interspeech, the AAAI Conference on Artificial Intelligence, and the Association for Computational Linguistics (ACL), has served as an area chair for Interspeech 2016, and as a reviewer of papers for several science conferences.

Innovations I find exciting

It is exciting to see how new techniques in deep learning continuously push the boundaries of the state of the art in the fields of dialogue and spoken language processing. I’m very interested in advances around unsupervised, semi-supervised and transfer learning, which allow deep learning models to leverage the power of large corpora without relying on costly and time-consuming manual annotations. Pre-trained language models like BERT and GPT-2 and their use in downstream applications are just a few examples. These innovations are particularly relevant for industry applications where scalability is key.

I am also excited about recent literature in deep learning that is allowing us to develop models to perform complex tasks like higher-level reasoning, for example, over the contents of a document or an image or both, as opposed to simpler classification tasks. I’m also excited to see how these methods can have a positive impact on people through their deployment in products, especially in applications of healthcare, accessibility and education.

Priya Ponnapalli, principal deep learning scientist

Priya Ponnapalli
Priya Ponnapalli, principal deep learning scientist

Ponnapalli is a senior manager and principal deep learning scientist within the Amazon ML Solutions Lab, where she leads a global team of data scientists that help AWS customers accelerate their adoption of ML and cloud technologies across industries, from healthcare and finance to sports. As the leader of Amazon ML Solutions Lab’s sports business, Ponnapalli works with customers including National Football League (NFL), Six Nations Rugby, and Formula 1 (F1), just to name a few, to enhance the fan experience and transform sports using ML.

Ponnapalli is also a senior research affiliate at the Genomic Signal Processing Lab at the University of Utah, and a faculty member at Rutgers Business School, where she teaches ML to business leaders, and works to inspire the next generation of leaders. Prior to joining AWS, she co-founded Eigengene, a data-driven personalized medicine startup and has helped companies like Genentech and Roche establish and build data science teams. For her PhD in electrical and computer engineering at the University of Texas at Austin, Ponnapalli defined and demonstrated the higher-order generalized singular value decomposition (HO GSVD), the only framework that can create a single coherent model from multiple two-dimensional datasets by extending the GSVD from two to more than two matrices.

Innovations I find exciting

As an Amazon ML Solutions Lab scientist, I’m most excited about real-world applications of ML across industries. I’m interested in innovations to overcome challenges with small, limited datasets that companies often have to contend with. I’m also intrigued by model interpretability and explainability which are key to earning trust and spurring broad adoption. I’m passionate about making ML accessible to all, so it can be used to solve some of the most important problems we are facing, from fighting climate change to treating cancer.

Ana Pinheiro Privette, senior program manager

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Ana Pinheiro Privette, senior program manager

Ana Pinheiro Privette is a senior program manager with Amazon's Sustainability group. She joined the Sustainability Science and Innovation team in September 2017 as the program lead for the Amazon Sustainability Data Initiative (ASDI), a program that seeks to leverage Amazon’s scale, technology, and infrastructure to help create more global innovation for sustainability. ASDI is a Tech-for-Good project and is a joint effort between Amazon Sustainability and the AWS Open Data team focusing on democratizing access to key data and analytical capabilities to anyone working in the sustainability space.

Privette was trained as an environmental engineer and as an earth sciences researcher at the New University of Lisbon (Portugal) and at MIT. She did her doctoral research work at NASA in the Washington D.C. area and as part of her project, she spent a couple of years running scientific field work sites in Africa to support a NASA international field campaign. After spending most of her career at NASA and NOAA as a scientist, Privette led projects for the White House climate portfolio, including the Obama Climate Data Initiative and the Partnership for Resilience and Preparedness (PREP), both focused on delivering better access and use of US Federal climate data to support decision makers.

Innovations I find exciting

As part of ASDI, I work very closely with AWS customers developing applications in the space of sustainability to understand what challenges they may be experiencing and how we may accelerate sustainability research and innovation by minimizing the cost and time required to acquire and analyze large sustainability datasets. The ASDI currently works with scientific organizations like NOAA, NASA, the UK Met Office and Government of Queensland to identify, host, and deploy key datasets on the AWS Cloud, including weather observations, weather forecasts, climate projection data, satellite imagery, hydrological data, air quality data, and ocean forecast data. These datasets are publicly available to anyone.

In addition, ASDI provides cloud grants to those interested in exploring the use of AWS’ technology and scalable infrastructure to solve big, long-term sustainability challenges with this data. The dual-pronged approach allows sustainability researchers to analyze massive amounts of data in mere minutes, regardless of where they are in the world or how much local storage space or computing capacity they can access.

Nashlie Sephus, manager, applied science

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Nashlie Sephus, applied scientist, Amazon Web Services machine learning team.
Credit: Terrence Wells@PoetWilliamsPhotography

Sephus is an applied scientist on AWS’s artificial intelligence team, focusing on computer vision. In this role, Sephus focuses on the fairness and accuracy of the team’s algorithms. Sephus formerly led the Amazon Visual Search team in Atlanta, which launched visual search for replacement parts on the Amazon Shopping app in June 2018. This technology was a result of former startup Partpic (Atlanta) being acquired by Amazon, for which she was the chief technology officer (CTO). Prior to working at Partpic, she received her PhD in 2014 from the School of Electrical and Computer Engineering at the Georgia Institute of Technology. She received her bachelor’s degree in computer engineering in 2007 from Mississippi State University.

Innovations I find exciting

Since the onset of machine learning and artificial intelligence, neural networks (such as convolutional neural networks (CNNs), and generative adversarial networks (GANs), etc.) and learning algorithms have always excited me. It’s being able to quickly and automatically draw patterns from data, whether it be images, video, or audio at scale, that fascinates me. Since music was my first love (along with karaoke!), music information retrieval has always been a passion of mine. These innovations, when used responsibly and fairly, are able to benefit people in their everyday activities.

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Job summaryWe are seeking a DSP Analytics scientist to further the development and application of analytics methods to examine the complex data flows of the DSP, and to translate deep-dives into actionable insights for our product teams. In this role you will develop new tools to analyze our advertising data to help improve the performance of our bidding algorithms, targeting and relevance systems, help advance our 3rd party and O&O supply strategy, and evaluate the adoption and impact of feature releases.Key job responsibilities· Analyze data trends regarding supply, optimization, ad load, and advertising mix effects that affect advertiser performance and contribute to achieving advertiser goals.· Present papers to senior leaders on issues like feature development impact on identity recognition rates, and changes of ad selection systems to improve fill rate highlighting insights that will inform our business development and engineering roadmaps.· Formalize our analytics approach to the DSP auctions by analyzing bid spreads, auction depth, and simulating impacts of potential auction structure changes.· Identify, standardize, and operationalize KPIs to effectively measure the performance of all systems involved in ad serving, and use trend insights to inform business priorities.· Partner with engineering teams to define data logging requirements and getting these prioritized in engineering roadmaps.· Validate financial models through analysis· Develop and own ad revenue and supply intelligence analytics decks that provide ongoing deep-divesA day in the lifeThe Senior DSP Analytics Scientist will work closely with business leaders and engineers on developing common data architecture that will optimize our data logging at different grains, and will allow data interoperability from bid flow to optimization to campaign delivery. The candidate will then analyze the data and present papers and ongoing reports on actionable insights.About the teamThe Ads Science Product Team's Mission: Work alongside those who need product data to apply objective perspective and business logic to uncover insights, advise strategic decisions, and adjust to industry changes
AU, ACT, Canberra
There's never been a more exciting time to join Amazon Australia!Who are we and what do we do?We are a world class ML team based in Adelaide and created in April 2020 with the hire of the Director of Applied Science, Anton van den Hengel.The Amazon ML AU team is developing state-of-the-art large-scale Machine Learning methods and applications involving terabytes of data. We work on applying machine learning, and particularly computer vision, to a wide spectrum of areas such as Amazon Retail, Seller Services, and Online Video. We also publish our research in the best venues internationally.The team is high performing, learning-oriented, motivated to over-achieve, have fun, and make history. We also have access to great data, and the best computing infrastructure.About Anton van den HengelAnton was the founding Director of The Australian Institute for Machine Learning (Australia’s largest machine learning research group), and is currently the Director of the Centre for Augmented Reasoning and a Professor of Computer Science at the University of Adelaide.With over 18,000 citations and an H-index of 67, Anton is one of the worlds’ leading authorities on Computer Vision and ML.About the TeamThe vast majority of the team have PhDs in machine learning (ML) or a related area from some of the best institutions globally, including Oxford, Stanford, Edinburgh, and Imperial College London, and have published in the best places in the field including Science, NeurIPS, IEEE PAMI, and CVPR.The team includes two world-class Principal Scientists and an Amazon Scholar. We value diversity and collaboration to help each other succeed as a team.Where are we based?Although the team is mainly Adelaide based, we support flexible working options blending at home and in office from our offices inAdelaide, Sydney, Melbourne, Canberra or Brisbane.Who are we looking for?We are seeking to add a Manager, ML Applied Science to an already awesome team.The Manager, ML Applied Science role at Amazon will be a technical team leader working to develop new challenging machine learning applications, services and platforms that optimize Amazon’s systems using cutting edge quantitative techniques.This is one of the most exciting machine learning job opportunities on the internet today!If you have deep technical know how in machine learning, know how to deliver, are highly innovative, are passionate about leading and growing a team with a combination of applied scientists and engineers and are long for the opportunity to build solutions to challenging problems that directly impact the company's bottom-line, we want to talk to you.What will I be working on?It’s fair to say that no two days are alike – so this position suits someone who enjoys variety and problem-solving:· Use machine learning, computer vision, data mining and statistical techniques to create new, scalable solutions for business problems· Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes· Design, develop and evaluate highly innovative models for predictive learning· Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation· Research, implement and publish novel machine learning and statistical approachesAdditional InformationWe have a number of current employees who split their time between lecturing at University and working for Amazon. Please let us know if this is of interest to you.We provide full visa sponsorship which is a relatively fast process as we have been successful in obtaining Distinguished Talent visas for this team (typically takes weeks rather than months).Full domestic and international relocation is also provided.About Amazon AustraliaAmazon offers great benefits including a competitive compensation and stock plan. We also look after our people with benefits including: subsidized private health and life insurance, commuter benefits and even an Amazon discount. Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, disability, age, or other legally protected status.For up to date news covering diversity and inclusion, sustainability and community engagement, please visit: https://www.aboutamazon.com.au/
AU, QLD, Brisbane
There's never been a more exciting time to join Amazon Australia!Who are we and what do we do?We are a world class ML team based in Adelaide and created in April 2020 with the hire of the Director of Applied Science, Anton van den Hengel.The Amazon ML AU team is developing state-of-the-art large-scale Machine Learning methods and applications involving terabytes of data. We work on applying machine learning, and particularly computer vision, to a wide spectrum of areas such as Amazon Retail, Seller Services, and Online Video. We also publish our research in the best venues internationally.The team is high performing, learning-oriented, motivated to over-achieve, have fun, and make history. We also have access to great data, and the best computing infrastructure.About Anton van den HengelAnton was the founding Director of The Australian Institute for Machine Learning (Australia’s largest machine learning research group), and is currently the Director of the Centre for Augmented Reasoning and a Professor of Computer Science at the University of Adelaide.With over 18,000 citations and an H-index of 67, Anton is one of the worlds’ leading authorities on Computer Vision and ML.About the TeamThe vast majority of the team have PhDs in machine learning (ML) or a related area from some of the best institutions globally, including Oxford, Stanford, Edinburgh, and Imperial College London, and have published in the best places in the field including Science, NeurIPS, IEEE PAMI, and CVPR.The team includes two world-class Principal Scientists and an Amazon Scholar. We value diversity and collaboration to help each other succeed as a team.Where are we based?Although the team is mainly Adelaide based, we support flexible working options blending at home and in office from our offices inAdelaide, Sydney, Melbourne, Canberra or Brisbane.Who are we looking for?We are seeking to add a Manager, ML Applied Science to an already awesome team.The Manager, ML Applied Science role at Amazon will be a technical team leader working to develop new challenging machine learning applications, services and platforms that optimize Amazon’s systems using cutting edge quantitative techniques.This is one of the most exciting machine learning job opportunities on the internet today!If you have deep technical know how in machine learning, know how to deliver, are highly innovative, are passionate about leading and growing a team with a combination of applied scientists and engineers and are long for the opportunity to build solutions to challenging problems that directly impact the company's bottom-line, we want to talk to you.What will I be working on?It’s fair to say that no two days are alike – so this position suits someone who enjoys variety and problem-solving:· Use machine learning, computer vision, data mining and statistical techniques to create new, scalable solutions for business problems· Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes· Design, develop and evaluate highly innovative models for predictive learning· Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation· Research, implement and publish novel machine learning and statistical approachesAdditional InformationWe have a number of current employees who split their time between lecturing at University and working for Amazon. Please let us know if this is of interest to you.We provide full visa sponsorship which is a relatively fast process as we have been successful in obtaining Distinguished Talent visas for this team (typically takes weeks rather than months).Full domestic and international relocation is also provided.About Amazon AustraliaAmazon offers great benefits including a competitive compensation and stock plan. We also look after our people with benefits including: subsidized private health and life insurance, commuter benefits and even an Amazon discount. Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, disability, age, or other legally protected status.For up to date news covering diversity and inclusion, sustainability and community engagement, please visit: https://www.aboutamazon.com.au/
AU, VIC, Melbourne
There's never been a more exciting time to join Amazon Australia!Who are we and what do we do?We are a world class ML team based in Adelaide and created in April 2020 with the hire of the Director of Applied Science, Anton van den Hengel.The Amazon ML AU team is developing state-of-the-art large-scale Machine Learning methods and applications involving terabytes of data. We work on applying machine learning, and particularly computer vision, to a wide spectrum of areas such as Amazon Retail, Seller Services, and Online Video. We also publish our research in the best venues internationally.The team is high performing, learning-oriented, motivated to over-achieve, have fun, and make history. We also have access to great data, and the best computing infrastructure.About Anton van den HengelAnton was the founding Director of The Australian Institute for Machine Learning (Australia’s largest machine learning research group), and is currently the Director of the Centre for Augmented Reasoning and a Professor of Computer Science at the University of Adelaide.With over 18,000 citations and an H-index of 67, Anton is one of the worlds’ leading authorities on Computer Vision and ML.About the TeamThe vast majority of the team have PhDs in machine learning (ML) or a related area from some of the best institutions globally, including Oxford, Stanford, Edinburgh, and Imperial College London, and have published in the best places in the field including Science, NeurIPS, IEEE PAMI, and CVPR.The team includes two world-class Principal Scientists and an Amazon Scholar. We value diversity and collaboration to help each other succeed as a team.Where are we based?Although the team is mainly Adelaide based, we support flexible working options blending at home and in office from our offices inAdelaide, Sydney, Melbourne, Canberra or Brisbane.Who are we looking for?We are seeking to add a Manager, ML Applied Science to an already awesome team.The Manager, ML Applied Science role at Amazon will be a technical team leader working to develop new challenging machine learning applications, services and platforms that optimize Amazon’s systems using cutting edge quantitative techniques.This is one of the most exciting machine learning job opportunities on the internet today!If you have deep technical know how in machine learning, know how to deliver, are highly innovative, are passionate about leading and growing a team with a combination of applied scientists and engineers and are long for the opportunity to build solutions to challenging problems that directly impact the company's bottom-line, we want to talk to you.What will I be working on?It’s fair to say that no two days are alike – so this position suits someone who enjoys variety and problem-solving:· Use machine learning, computer vision, data mining and statistical techniques to create new, scalable solutions for business problems· Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes· Design, develop and evaluate highly innovative models for predictive learning· Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation· Research, implement and publish novel machine learning and statistical approachesAdditional InformationWe have a number of current employees who split their time between lecturing at University and working for Amazon. Please let us know if this is of interest to you.We provide full visa sponsorship which is a relatively fast process as we have been successful in obtaining Distinguished Talent visas for this team (typically takes weeks rather than months).Full domestic and international relocation is also provided.About Amazon AustraliaAmazon offers great benefits including a competitive compensation and stock plan. We also look after our people with benefits including: subsidized private health and life insurance, commuter benefits and even an Amazon discount. Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, disability, age, or other legally protected status.For up to date news covering diversity and inclusion, sustainability and community engagement, please visit: https://www.aboutamazon.com.au/
US, WA, Seattle
Job summaryThe Sustainability Science & Innovation (SSI) team is both the scientific authority for sustainability at Amazon and its sustainability research and development team. The Materials Science and Innovation (MSI) team is a new and growing team within SSI focused on sustainable materials research, development, and commercialization of new technologies. We inform Amazon’s most senior leaders on Think Big opportunities around materials and sustainability so that they may place big bets that will help us take care of the planet, benefit customers, and enable continued business growth. We also work closely with academic, government, and other industry partners to collaborate on developing new materials solutions that scale beyond Amazon applications alone.We are looking for an experienced Sr. Scientist to help us develop and drive innovative scientific solutions that will improve the sustainability of materials in our products, packaging, operations, and infrastructure. In this role, you will lead applied research that is focused on decarbonization of the full life cycle of polymers. You will also partner with internal stakeholders on our business teams to rapidly transition new materials technologies to real-world applications.In addition to developing new, innovative materials solutions, you will also serve as an internal subject matter expert, helping our business teams understand and make informed, science-based decisions related to the materials they use.Key job responsibilities• Develop and lead early-stage, strategic sustainable materials initiatives and effectively influence, negotiate, and communicate with stakeholders• Manage innovation projects from ideation to commercialization• Engage with external thought leaders and companies developing innovative materials solutions• Stay current on new developments in the field of polymers and sustainability• Provide subject matter expertise to time-critical questions from multiple business teams• Professionally communicate to senior business leaders
US, WA, Seattle
Job summaryThe Sustainability Science & Innovation (SSI) team is both the scientific authority for sustainability at Amazon and its sustainability research and development team. The Materials Science and Innovation (MSI) team is a new and growing team within SSI focused on sustainable materials research, development, and commercialization of new technologies. We inform Amazon’s most senior leaders on Think Big opportunities around materials and sustainability so that they may place big bets that will help us take care of the planet, benefit customers, and enable continued business growth. We also work closely with academic, government, and other industry partners to collaborate on developing new materials solutions that scale beyond Amazon applications alone.We are looking for an experienced Sr. Scientist to help us develop and drive innovative scientific solutions that will improve the circularity of materials in our products, packaging, operations, and infrastructure. In this role, you will help conceptualize and develop new recycling technologies based on a deep understanding of materials. This is a dynamic role that will bring together materials science, chemical engineering, chemistry, and other scientific disciplines to devise and develop ways to keep materials in use. You will also partner with academic, government, and industries leaders to help develop solutions that scale beyond Amazon.In addition to helping develop new innovative materials solutions, you will also serve as an internal subject matter expert, helping our business teams understand and make informed decisions related to materials recycling technologies.Key job responsibilities• Develop and lead early-stage, strategic sustainable materials initiatives and effectively influence, negotiate, and communicate with stakeholders• Manage innovation projects from ideation to commercialization• Engage with external thought leaders and companies developing innovative recycling solutions• Stay current on new developments in the field of materials and advanced recycling technologies• Provide subject matter expertise to time-critical questions from multiple business teams• Professionally communicate to senior business leadersA day in the life