Five ways the ABACUS label advances nature-based carbon removal

From more-accurate measurement of carbon dioxide removal to greater diversity in restoration design, the ABACUS label’s requirements help advance the integrity of restoration projects in the voluntary carbon market.

Amazon cofounded the Climate Pledge in 2019 to commit to reaching net-zero carbon by 2040. The first priority of the pledge is to implement decarbonization strategies — in line with the Paris Agreement — through operational changes such as improving efficiency, driving forward scalable carbon-free energy sources, reducing waste, and innovating materials.

However, alongside real business change that directly reduces greenhouse gas emissions, there is also need for large-scale investments in climate change mitigation outside of our value chain (what we call carbon neutralization). At Amazon, we do this through both nature-based solutions and technological carbon dioxide removal.

Nature-based carbon removal harnesses the power of photosynthesis to sequester carbon in natural and managed ecosystems. This means altering land management in alignment with nature through native reforestation, agroforestry, and other forms of high-quality restoration. These activities alone have the potential to remove 2–4 billion tons of carbon per year; that’s almost half of the estimated 5–10 billion tons per year that experts estimate is likely needed through the end of the century in order to keep our global temperatures at safe levels.

While the voluntary carbon market has the potential to bring billions of dollars of finance to restoration projects, less than 3% of credits issued to date come from nature-based carbon removal. This is due to the voluntary carbon market’s prices’ falling below the costs of high-quality nature-based restoration.

That’s where ABACUS comes in. ABACUS is a set of principles and requirements, codified within Verra’s Verified Carbon Standard, that helps advance the integrity of restoration projects within the voluntary carbon market. ABACUS was developed by a working group of expert practitioners, conservation professionals, and scientists — including Amazon’s own carbon neutralization scientists — in an effort to raise the quality bar for agroforestry and native-restoration projects. The ABACUS label has already begun to raise the quality bar for leading buyers.

Below are five big ideas within ABACUS that help raise the bar on scientific rigor and transparency.

  1. Dynamic baseline to measure additionality

    Historically, restoration carbon projects assume that whatever land use was occurring before a project takes place — pasture or agriculture, for example — would have continued unaltered without the project intervention. This assumption ignores the myriad ecological, economic, and policy dynamics that could affect carbon removal without assistance from the voluntary carbon market.

    Related content
    Investing in 500+ solar and wind projects, bringing carbon-free energy to dirty grids, and buying Renewable Energy Certificates all played a role.

    In addition to demonstrating that a project would not be viable without carbon credit finance, ABACUS requires a treatment-control approach to measuring additionality, or the carbon removal resulting from the project above and beyond what would have occurred otherwise. This means matching the project “treatment” area — based on historical, satellite-based proxies for biomass — to a population of “control” plots that are followed through time. Each of these controls represents a potential alternate reality for the project in the absence of restoration.

    If the control plots regain forest carbon at pace with the project, this indicates that the project may have regained forest carbon on its own, without the intervention. If the control plots remain low-carbon, degraded land, we can be more confident that the project’s climate impacts are additional. By treating additionality as dynamic instead of static, we’re able to obtain a more data-driven estimation of the true impact of restoration.

  2. Carbon projects as engines for agricultural production

    Carbon removal cannot come at the expense of food production; in fact, these challenges are inextricably linked. Under some projections, agricultural production will need to double by 2050, even as the least productive pasture and croplands are restored to forest cover. Sustainably intensifying agriculture to increase food production, while sparing land for carbon removal — or, better, integrating carbon removal within productive agricultural systems — is critical to reconciling these needs.

    Drone footage of a mature cocoa, coconut, and mahogany agroforestry system, adjacent to a degraded pasture in southeast Pará, Brazil.
    ABACUS seeks to restore degraded pasturelands to diverse agroforestry systems like this one. (Drone footage courtesy of Eric Plançon)

    But the voluntary carbon market is not equipped to tackle this challenge. Carbon removal projects that displace agricultural production often result in indirect land use change and associated emissions, as agricultural markets replace lost production to serve growing demand (“leakage”).

    These crop- and region-specific leakage effects are difficult to quantify reliably. Conventional leakage methodologies impose standardized deductions based on default carbon leakage rates when agricultural production is displaced. This creates a persistent source of uncertainty and risk of over-crediting, and the approach misses an opportunity to build synergies between restoration and agricultural production.

    Related content
    From investing in new carbon-free energy projects to advocating for grid modernization and collaborating with key stakeholders around the world, Amazon is working toward a cleaner energy future.

    ABACUS instead takes a “food-forward” approach to leakage accounting. Rather than using an imprecise default value to quantify leakage effects, ABACUS requires projects to eliminate leakage by maintaining or enhancing agricultural production in the project areas and surrounding landscapes. By recognizing the land-sparing effect of enhancing production of different types of commodities, ABACUS encourages projects to co-optimize for carbon and agricultural production and avoids locking regions into specific agricultural products. The working group is engaging partners to create commodity-specific leakage metrics based on land-carbon “opportunity costs” to estimate, and mitigate, the impacts of leakage.

  3. Abbreviated crediting periods for durability assurance

    Carbon stored in ecosystems can be highly durable, but it faces persistent, long-term climate risks such as fire, drought, and land use change, which must be responsibly managed. Nature-based carbon removal should seek “effective permanence” — an actual net greenhouse gas benefit to the atmosphere that is equal to, or greater than, the net benefit represented by the credits. In addition, the removal should ensure that this balance can be maintained indefinitely.

    On the other hand, agroforestry and restoration projects can catalyze shifts to land use systems that durably enhance carbon storage even beyond what is credited. This can happen through spillover effects, continued carbon removal after the crediting period, and biophysical cooling feedbacks, among other factors. ABACUS includes several methods that improve the likelihood that nature-based carbon remains durably stored — for example, requiring projects to plant ecologically appropriate restoration systems and to create public plans for the longevity of project activities even after the support of carbon revenues.

    Related content
    Amazon teams up with RTI International, Schlumberger, and International Paper on a project selected by the US Department of Energy to scale carbon capture and storage for the pulp and paper industry.

    One of ABACUS’s key innovations is to limit the crediting period in an effort to maximize uncredited removals. The ABACUS working group found that revenues from credits generated beyond year 30 are mostly immaterial to investment decisions today, due to their heavy discounts. By shortening the crediting period to 40 years maximum — as opposed to as much as 100 years under some voluntary carbon market standards — ABACUS will create a source of uncredited carbon removal that can serve as an additional buffer against future reversals.

    Additionally, ABACUS proposes that projects will be required to allocate a portion of carbon credits issued late in the crediting period (i.e., years 31–40) to a “long-term permanence mechanism” such as an enhanced buffer pool or insurance product. Achieving increased confidence in the effective permanence of nature-based carbon credits may require stringing together removals or replacing a moderate-durability credit with a high-durability credit, if and when previously credited removals are reversed. Economically, such a construct is currently likely to be cost effective compared to today’s high-durability carbon dioxide removal.

  4. Going beyond commercial monoculture plantations

    Forest plantations already cover nearly 300 million hectares globally — roughly equivalent to the entire area of India. That figure has more than doubled in the last 30 years, without a robust voluntary carbon market, and it is projected to continue growing to provide timber, pulpwood, firewood, and charcoal to increasing populations and a growing economy.

    Brazil_Drone.png
    Orthorectified mosaic capturing a range of land management types on a typical farm in the Amazon basin, Brazil. We can see the contrast between low-carbon-density pasture (left) and diverse agroforestry (center), which combines shade-tolerant commodity production with native, carbon-rich hardwood trees. ABACUS is designed to support native restoration and agroforestry interventions on formerly forested, degraded land.
    Photos captured and combined by ICRAF-Brazil on behalf of the Agroforestry Accelerator.

    As a first step, ABACUS prohibits most monocultures and requires project developers to use observed or modeled data to demonstrate that planted systems are ecologically appropriate for the landscape. This approach avoids projects seeking to reforest with systems that aren’t suitable for the location’s native biomass potential — a function of climate, soil type, water availability, and elevation, among other things. Credit buyers are encouraged to send demand signals that further encourage biodiverse, ecologically sound, and socially beneficial restoration.

  5. Transparency to foster competition on quality

    For some aspects of restoration, it’s challenging to prescribe universally applicable requirements without stifling innovation and local knowledge: every restored ecosystem is unique in its own way. ABACUS introduces multiple requirements for added transparency that will allow buyers, investors, and the public to better assess for themselves the effectiveness of project designs and measurement.

    Related content
    Amazon advocates for updating carbon accounting to measure where renewable-energy projects will have the greatest impact.

    For example, ABACUS projects will need to publish their in-situ inventory measurements, systematically justify their use of allometric or other scaling models, and report on design approaches to avoid measurement or sampling bias. Instead of once every five years or so, ABACUS requires projects to annually map disturbances, to ensure that carbon credited and subsequently reversed is immediately identified. With enhanced transparency, the ABACUS working group hopes to incentivize project developers to compete on quality.

  6. ABACUS doesn’t solve all of the challenges of quantifying the complete climate impact of nature-based carbon removal, and it is no replacement for the stakeholder engagement necessary to ensure genuine socio-economic benefits on the ground. Many important improvements remain for future versions of the label’s principles and requirements. As we learn, the ABACUS working group will continue to enhance the scientific rigor of and public confidence in ecosystem restoration, catalyzing rural restoration economies and livelihoods and — if we succeed — helping to enable billions of tons of ecosystem carbon removal across the world.

Research areas

Related content

US, CA, Santa Clara
AWS AI is looking for passionate, talented, and inventive Research Scientists with a strong machine learning background to help build industry-leading Conversational AI Systems. Our mission is to provide a delightful experience to Amazon’s customers by pushing the envelope in Natural Language Understanding (NLU), Dialog Systems including Generative AI with Large Language Models (LLMs) and Applied Machine Learning (ML). As part of our AI team in Amazon AWS, you will work alongside internationally recognized experts to develop novel algorithms and modeling techniques to advance the state-of-the-art in human language technology. Your work will directly impact millions of our customers in the form of products and services that make use language technology. You will gain hands on experience with Amazon’s heterogeneous text, structured data sources, and large-scale computing resources to accelerate advances in language understanding. We are hiring in all areas of human language technology: NLU, Dialog Management, Conversational AI, LLMs and Generative AI. About the team Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Utility Computing (UC) AWS Utility Computing (UC) provides product innovations — from foundational services such as Amazon’s Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), to consistently released new product innovations that continue to set AWS’s services and features apart in the industry. As a member of the UC organization, you’ll support the development and management of Compute, Database, Storage, Internet of Things (IoT), Platform, and Productivity Apps services in AWS, including support for customers who require specialized security solutions for their cloud services. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.
US, VA, Herndon
Machine learning (ML) has been strategic to Amazon from the early years. We are pioneers in areas such as recommendation engines, product search, eCommerce fraud detection, and large-scale optimization of fulfillment center operations. The Generative AI team helps AWS customers accelerate the use of Generative AI to solve business and operational challenges and promote innovation in their organization. As an applied scientist, you are proficient in designing and developing advanced ML models to solve diverse challenges and opportunities. You will be working with terabytes of text, images, and other types of data to solve real-world problems. You'll design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. We’re looking for talented scientists capable of applying ML algorithms and cutting-edge deep learning (DL) and reinforcement learning approaches to areas such as drug discovery, customer segmentation, fraud prevention, capacity planning, predictive maintenance, pricing optimization, call center analytics, player pose estimation, event detection, and virtual assistant among others. Key job responsibilities The primary responsibilities of this role are to: • Design, develop, and evaluate innovative ML models to solve diverse challenges and opportunities across industries • Interact with customer directly to understand their business problems, and help them with defining and implementing scalable Generative AI solutions to solve them • Work closely with account teams, research scientist teams, and product engineering teams to drive model implementations and new solution About the team ABOUT AWS: 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
Our team's mission is to improve Shopping experience for customers interacting with Amazon devices via voice. We research and develop advanced state-of-the-art speech and language modeling technologies. Do you want to be part of the team developing the latest technology that impacts the customer experience of ground-breaking products? Then come join us and make history. Key job responsibilities We are looking for a passionate, talented, and inventive Applied Scientist with a background in Machine Learning to help build industry-leading Speech and Language technology. As an Applied Scientist at Amazon you will work with talented peers to develop novel algorithms and modelling techniques to drive the state of the art in speech synthesis. Position Responsibilities: * Participate in the design, development, evaluation, deployment and updating of data-driven models for Speech and Language applications. * Participate in research activities including the application and evaluation of Speech and Language techniques for novel applications. * Research and implement novel ML and statistical approaches to add value to the business. * Mentor junior engineers and scientists.
CN, 31, Shanghai
The AWS Shanghai AI Lab is looking for a passionate, talented, and inventive staff in all AI domains with a strong machine learning background as an Applied Scientist. Founded in 2018, the Shanghai Lab has been an innovation center of for long-term research projects across domains as machine learning, computer vision, natural language processing, and open-source AI system. Meanwhile, these incubated projects power products across various AWS services. As part of the lablet, you will take a leadership role and join a vibrant team with a diverse set of expertise in both machine learning and applicational domains. You will work on state-of-the-art solutions on fundamental research problems with other world-class scientists and engineers in AWS around the globe and across the boarders. You will have the responsibility to design and innovate solutions to our customers. You will build models to tame large amount of data, achieve industry-level scalability and efficiency, and along the way rapidly grow and build the team.
US, WA, Bellevue
Amazon is looking for an outstanding Senior Economist to help build next generation selection/assortment systems. On the Specialized Selection team within the Supply Chain Optimization Technologies (SCOT) organization, we own the selection to determine which products Amazon offers in our fastest delivery programs. We build tools and systems that enable our partners and business owners to scale themselves by leveraging our problem domain expertise, focusing instead on introspecting our outputs and iteratively helping us improve our ML models rather than hand-managing their assortment. We partner closely with our business stakeholders as we work to develop state-of-the-art, scalable, automated selection. Our team is highly cross-functional and employs a wide array of scientific tools and techniques to solve key challenges, including supervised and unsupervised machine learning, non-convex optimization, causal inference, natural language processing, linear programming, reinforcement learning, and other forecast algorithms. Some critical research areas in our space include modeling substitutability between similar products, incorporating basket awareness and complementarity-aware logic, measuring speed sensitivity of products, modeling network capacity constraints, and supply and demand forecasting. We're looking for a candidate with a background in experiment design and causal analysis to lead studies related to selection and speed. Potential projects include understanding the short-term and long-term customer impact of assortment changes across different speed. As an Senior Economist, you'll build econometric models using our world-class data systems and apply economic theory to solve business problems in a fast-moving environment. You will work with software engineers, product managers, and business teams to understand the business problems and requirements, distill that understanding to crisply define the problem, and design and develop innovative solutions to address them. To be successful in this role, you'll need to communicate effectively with product and tech teams, and translate data-driven findings into actionable insights. You'll thrive if you enjoy tackling ambiguous challenges using the economics toolkit and identifying and solving problems at scale. We have a supportive, fast-paced team culture, and we prioritize learning, growth, and helping each other continuously raise the bar. Key job responsibilities - Lead data-driven econometric studies to create future business opportunities - Consult with stakeholders in Selection and other teams to help solve existing business challenges - Independently identify and pursue new opportunities to leverage economic insights - Advise senior leaders and collaborate with other scientists to drive innovation - Support innovative delivery program growth worldwide - Write business and technical documents communicating business context, methods, and results to business leadership and other scientists - Serve as a technical lead and mentor for junior scientists, ensuring a high science bar - Serve as a technical reviewer for our team and related teams, including document and code reviews
US, CA, Pasadena
The Amazon Web Services (AWS) Center for Quantum Computing in Pasadena, CA, is looking to hire a Research Scientist specializing the design of microwave components for cryogenic environments. Working alongside other scientists and engineers, you will design and validate hardware performing microwave signal conditioning at cryogenic temperatures for AWS quantum processors. Candidates must have a background in both microwave theory and implementation. Working effectively within a cross-functional team environment is critical. The ideal candidate will have a proven track record of hardware development from requirements development to validation. Key job responsibilities Our scientists and engineers collaborate across diverse teams and projects to offer state of the art, cost effective solutions for the signal conditioning of AWS quantum processor systems at cryogenic temperatures. You’ll bring a passion for innovation, collaboration, and mentoring to: Solve layered technical problems across our cryogenic signal chain. Develop requirements with key system stakeholders, including quantum device, test and measurement, cryogenic hardware, and theory teams. Design, implement, test, deploy, and maintain innovative solutions that meet both performance and cost metrics. Research enabling technologies necessary for AWS to produce commercially viable quantum computers. A day in the life As you design and implement cryogenic microwave signal conditioning solutions, from requirements definition to deployment, you will also: Participate in requirements, design, and test reviews and communicate with internal stakeholders. Work cross-functionally to help drive decisions using your unique technical background and skill set. Refine and define standards and processes for operational excellence. Work in a high-paced, startup-like environment where you are provided the resources to innovate quickly. About the team AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.
US, CA, San Francisco
We are seeking a highly motivated PhD Research Scientist Intern to join our robotics teams at Amazon. This internship offers a unique opportunity to work on cutting-edge robotics projects that directly impact millions of customers worldwide. You will collaborate with world-class experts, tackle groundbreaking research problems, and contribute to the development of innovative solutions that shape the future of robotics and artificial intelligence. As a Research Scientist intern, you will be challenged to apply theory into practice through experimentation and invention, develop new algorithms using modeling software and programming techniques for complex problems, implement prototypes, and work with massive datasets. You'll find yourself at the forefront of innovation, working with large language models, multi-modal models, and modern reinforcement learning techniques, especially as applied to real-world robots. Imagine waking up each morning, fueled by the excitement of solving intricate puzzles that have a direct impact on Amazon's operational excellence. Your day might begin by collaborating with cross-functional teams, exchanging ideas and insights to develop innovative solutions in robotics and AI. You'll then immerse yourself in a world of data and algorithms, leveraging your expertise in large language models and multi-modal systems to uncover hidden patterns and drive operational efficiencies. Throughout your journey, you'll have access to unparalleled resources, including state-of-the-art computing infrastructure, cutting-edge research papers, and mentorship from industry luminaries. This immersive experience will not only sharpen your technical skills but also cultivate your ability to think critically, communicate effectively, and thrive in a fast-paced, innovative environment where bold ideas are celebrated. Amazon has positions available for Research Scientist Internships in, but not limited to, Bellevue, WA; Boston, MA; Cambridge, MA; New York, NY; Santa Clara, CA; Seattle, WA; Sunnyvale, CA, and San Francisco, CA. We are particularly interested in candidates with expertise in: Robotics, Computer Vision, Artificial Intelligence, Causal Inference, Time Series, Large Language Models, Multi-Modal Models, and Reinforcement Learning. In this role, you gain hands-on experience in applying cutting-edge analytical and AI techniques to tackle complex business challenges at scale. If you are passionate about using data-driven insights and advanced AI models to drive operational excellence in robotics, we encourage you to apply. The ideal candidate should possess the ability to work collaboratively with diverse groups and cross-functional teams to solve complex business problems. A successful candidate will be a self-starter, comfortable with ambiguity, with strong attention to detail, and have the ability to thrive in a fast-paced, ever-changing environment. A day in the life Work alongside global experts to develop and implement novel scalable algorithms in robotics, incorporating large language models and multi-modal systems. Develop modeling techniques that advance the state-of-the-art in areas of robotics, particularly focusing on modern reinforcement learning for real-world robotic applications. Anticipate technological advances and work with leading-edge technology in AI and robotics. Collaborate with Amazon scientists and cross-functional teams to develop and deploy cutting-edge robotics solutions into production, leveraging the latest in language models and multi-modal AI. Contribute to technical white papers, create technical roadmaps, and drive production-level projects that support Amazon Science in the intersection of robotics and advanced AI. Embrace ambiguity, maintain strong attention to detail, and thrive in a fast-paced, ever-changing environment at the forefront of AI and robotics research.
US, WA, Seattle
Here at Amazon, we embrace our differences. We are committed to furthering our culture of diversity and inclusion of our teams within the organization. How do you get items to customers quickly, cost-effectively, and—most importantly—safely, in less than an hour? And how do you do it in a way that can scale? Our teams of hundreds of scientists, engineers, aerospace professionals, and futurists have been working hard to do just that! We are delivering to customers, and are excited for what’s to come. Check out more information about Prime Air on the About Amazon blog (https://www.aboutamazon.com/news/transportation/amazon-prime-air-delivery-drone-reveal-photos). If you are seeking an iterative environment where you can drive innovation, apply state-of-the-art technologies to solve real world delivery challenges, and provide benefits to customers, Prime Air is the place for you. Come work on the Amazon Prime Air Team! Our Prime Air Drone Vehicle Design and Test team within Flight Sciences is looking for an outstanding engineer to help us rapidly configure, design, analyze, prototype, and test innovative drone vehicles. You’ll be responsible for assessing the Aerodynamics, Performance, and Stability & Control characteristics of vehicle designs. You’ll help build and utilize our suite of Multi-disciplinary Optimization (MDO) tools. You’ll explore new and novel drone vehicle conceptual designs in both focused and wide open design spaces, with the ultimate goal of meeting our customer requirements. You’ll have the opportunity to prototype vehicle designs and support wind tunnel and other testing of vehicle designs. You will directly support the Office of the Chief Program Engineer, and work closely across all vehicle subsystem teams to ensure integrated designs that meet performance, reliability, operability, manufacturing, and cost requirements. About the team Our Flight Sciences Vehicle Design & Test organization includes teams that span the following disciplines: Aerodynamics, Performance, Stability & Control, Configuration & Spatial Integration, Loads, Structures, Mass Properties, Multi-disciplinary Optimization (MDO), Wind Tunnel Testing, Noise Testing, Flight Test Instrumentation, and Rapid Prototyping.
US, WA, Seattle
This is a unique opportunity to build technology and science that millions of people will use every day. Are you excited about working on large scale Natural Language Processing (NLP), Machine Learning (ML), and Large Language Models (LLM)? We are embarking on a multi-year journey to improve the shopping experience for customers using Alexa globally. In 2024, we started building all Shopping experiences leveraging LLMs in the US. We create customer-focused solutions and technologies that makes shopping delightful and effortless for our customers. Our goal is to understand what customers are looking for in whatever language happens to be their choice at the moment and help them find what they need in Amazon's vast catalog of billions of products. We are seeking an Applied Scientist to lead a new, greenfield initiative that shapes the arc of invention with Machine Learning and Large Language Models. Your deliverables will directly impact executive leadership team goals and shape the future of shopping experiences with Alexa. We’re working to improve shopping on Amazon using the conversational capabilities of LLMs, and are searching for pioneers who are passionate about technology, innovation, and customer experience, and are ready to make a lasting impact on the industry. You'll be working with talented scientists, engineers, across the breadth of Amazon Shopping and AGI to innovate on behalf of our customers. If you're fired up about being part of a dynamic, driven team, then this is your moment to join us on this exciting journey!
US, WA, Seattle
The vision for Alexa is to be the world’s best personal assistant. Such an assistant will play a vital role in managing the communication lives of customers, from drafting communications to coordinating with people on behalf of customers. At Alexa Communications, we’re leveraging Generative AI to bring this vision to life. If you’re passionate about building magical experiences for customers, while solving hard, complex technical problems, then this role is for you. You will operate at the intersection of large language models, real time communications, voice and graphical user interfaces, and mixed reality to deliver cutting-edge features for end users. Come join us to invent the future of how millions of customers will communicate with and through their virtual AI assistants. Key job responsibilities The Comms Experience Insights (CXI) team is looking for an experienced, self-driven, analytical, and strategic Data Scientist II. We are looking for an individual who is passionate about tying together huge amounts of data to answer complex stakeholder questions. You should have deep expertise in translating data into meaningful insights through collaboration with Data Engineers and Business Analysts. You should also have extensive experience in model fitting and explaining how the insights derived from those models impact a business. In this role, you will take data curated by a dedicated team of Data Engineers to conduct deep statistical analysis on usage trends. The right candidate will possess excellent business and communication skills, be able to work with business owners to develop and define key business questions, and be able to collaborate with Data Engineers and Business Analysts to analyze data that will answer those questions. The right candidate should have a solid understanding of how to curate the right datasets that can be used to train data models, and the desire to learn and implement new technologies and services to further a scalable, self-service model.