The life of a prescription at Amazon Pharmacy

From pricing estimation and regulatory compliance to inventory management and chatbot assistants, machine learning models help Amazon Pharmacy customers stay healthy and save time and money.

Pharmacies play a vital role in ensuring patients’ health, but the process of dispensing medications is far more complex than it may appear. At Amazon Pharmacy, we are using artificial intelligence (AI) and cutting-edge technologies to remove this complexity and improve patients’ experiences.

The pharmacy challenge

When a prescription arrives at a pharmacy, its details must be entered into the pharmacy's software system. Then, a licensed pharmacist reviews the prescription to verify the patient's information, check for potential drug interactions or allergies, and confirm that the prescribed medication, dosage, and instructions are appropriate and accurate.

This process is susceptible to errors — even if the prescription arrives electronically. A U.S. study estimated that there are approximately 51.5 million dispensing errors annually in community pharmacies, with a meta-analysis supporting an error rate of around 1.5%.

Related content
Amazon Health Services' Sunita Mishra and Columbia University’s Katrina Armstrong discuss technology's potential role in medical settings.

Pharmacies must also handle billing and insurance claims for their patients. These are involved calculations based on patients’ specific insurance policies, their copay responsibilities, and the rates that the pharmacy has negotiated with different insurance providers. In fact, just identifying a patient's insurance can be challenging. The result is that patients often do not know the prices they will pay for their medications until the end of the process, when they’re picking up their prescriptions at a retail pharmacy or checking out online.

After a prescription has been validated and the purchase completed, the pharmacy staff must locate the specific medication in their inventory. However, it's possible that at this stage, the prescribed medication, the required strength, or the preferred brand may no longer be available. Providing a substitute could necessitate contacting the prescribing physician again for approval. Additionally, depending on the substitution, the billing and insurance process may need to be re-initiated to account for any changes in pricing or coverage.

Once the medication is ready for dispensing, the pharmacist provides the patient with detailed instructions on how to properly take it. Patients may also have questions regarding their insurance coverage or costs. However, these conversations often take place in public areas, which can be uncomfortable for patients who have personal or sensitive questions.

Finally, patients need convenient access to pharmacists at any time, day or night. This allows patients to report how they are feeling while taking their medications, which can help pharmacists provide better guidance and support throughout the treatment process.

The AI-powered pharmacy

Amazon Pharmacy uses large language models (LLMs) to enhance the accuracy, safety, and speed of prescription processing. First, we use LLMs to transcribe raw prescription data into structured, standardized formats that are seamlessly processed by software and more easily understood by patients. For example, medical abbreviations like "PRN" and "QID" are transformed into their full-text equivalents, such as "take as needed" and "take four times a day," respectively.

After standardizing the prescription data, the system performs a validation step that includes checking the medication names, dosage forms, strengths, and directions for use against an industry database. After validation, all prescriptions are still carefully reviewed and verified by licensed pharmacists. By leveraging this automated process, Amazon Pharmacy has reduced the number of near-miss events (potential medication errors) by 50% and improved processing speed by up to 90%. This allows our pharmacists to focus their time and attention on critical tasks, such as providing personalized care and addressing complex medication-related issues.

Pharmacy.png
The AI workflow at Amazon Pharmacy.

Amazon Pharmacy understands the importance of price transparency for customers. When a patient is using insurance to cover the cost of medication, Amazon Pharmacy will first try to obtain the exact price directly from the insurance provider. However, if this real-time pricing information is not available, Amazon Pharmacy will provide an estimated out-of-pocket cost for the patient's copay, without requiring the customer to go through the entire checkout process first.

To generate accurate price estimates, Amazon Pharmacy uses an ensemble of decision-tree-based models. These models take into account factors such as historical claims data (time series features) and static information such as the specific medication, the number-of-days' supply, and the quantity prescribed. By providing upfront pricing information, either the exact cost or a reliable estimate, Amazon Pharmacy aims to increase transparency and help patients understand their out-of-pocket expenses before committing to purchases. Additionally, Amazon Pharmacy searches for applicable industry coupons and automatically applies them to orders. We also use ML to validate the patient's insurance registration and claim requests to insurance providers.

Amazon is known for its extensive logistics and fulfillment capabilities, and Amazon Pharmacy takes advantage of Amazon's vast network of same-day and local delivery facilities, as well as innovative transportation methods like Prime Air drones. Additionally, Amazon Pharmacy employs specialized automation technologies, such as robotic vial-filling systems, to streamline the medication-dispensing process, enabling prompt delivery of medications to patients across the nation.

Beyond the physical logistics infrastructure, Amazon Pharmacy has developed its own order fulfillment system to handle complex medication-routing and -dispensing logic, while ensuring compliance with over 160 different pharmacy regulatory bodies across the United States. For example, if a medication for an order is no longer available at the closest fulfillment center, Amazon Pharmacy can identify the next best eligible facility to fulfill the order, even if it's in a different state, provided that the relevant state regulations allow for such cross-state fulfillment.

Related content
ARA recipient Marinka Zitnik is focused on how machine learning can enable accurate diagnoses and the development of new treatments and therapies.

Moreover, Amazon Pharmacy's order fulfillment algorithm takes into account regional variations in insurance eligibility. In such cases, Amazon Pharmacy will first validate that there are no changes to the patient's copay. If changes are required, Amazon Pharmacy will work with the insurance providers to clarify the benefits. To accomplish all of this, Amazon Pharmacy's order fulfillment solution employs a combination of operations research techniques, such as optimization solvers, and deep-learning models such as variational autoencoders and diffusion models. These models help simulate different scenarios and optimize the fulfillment process to ensure efficient and compliant delivery of medications to patients.

Amazon Pharmacy also introduced personalized AI-powered chatbots to assist users. These virtual assistants can answer frequently asked questions about Amazon Pharmacy, such as how to enroll in the service. In a first for the industry, Amazon Pharmacy's chatbot also provides personalized support, allowing patients to ask questions about their medication orders, delivery status, prescription transfers, and inventory availability. If a patient prefers, there is always 24/7 access to direct pharmacist support and the customer care team.

Implementing a personalized AI chatbot in the healthcare setting is a complex task. It's crucial to safeguard patients' privacy and ensure the highest level of accuracy, avoiding LLM hallucinations. To address these challenges, Amazon Pharmacy has enhanced the typical retrieval-augmented generation (RAG) approach used for LLM chatbots. The enhancements include input and output guardrails, the use of ensembles of specialized (mini) AI models, and a continuous model improvement process through reinforcement learning using human feedback (RLHF).

The digital pharmacy counter

Amazon Pharmacy is leveraging ML and optimization algorithms to streamline the complex process of dispensing medications. By addressing long-standing challenges such as data entry errors, lack of price transparency, intelligent nationwide medication fulfillment, and personalized-AI-based experiences, Amazon Pharmacy enables patients to save time, save money, and stay healthy.

Related content
Gari Clifford, the chair of the Department of Biomedical Informatics at Emory University and an Amazon Research Award recipient, wants to transform healthcare.

For example, it is not uncommon for medications to go into short-term back order (STBO), especially new medications entering the market, as was recently the case with the GLP-1 line of medications used for diabetes and weight loss. Amazon Pharmacy’s intelligent-fulfillment solution has enabled an 85% decrease in delivery estimate misses for unforeseen reasons, including STBOs.

The AI-powered Amazon Pharmacy assistant helps customers navigate the complexities of the pharmacy industry, providing 24/7 assistance on topics like prescription tracking, insurance coverage, medication availability, and cost-saving strategies. Half of the customers who interact with the assistant don't require additional human support, which saves them time and effort. (For customers who still need assistance, Amazon Pharmacy likewise provides 24/7 access to pharmacist support.) Additionally, the assistant provides real-time medication transfer or shipment status updates in response to patient queries, handling follow-up questions to recommend next steps.

For all the success of our AI-based systems, however, Amazon Pharmacy’s research and engineering teams remain hard at work. We will continue pushing the envelope in scaling medication dispensing, improving personalized AI-based chatbots and assistants, and transitioning toward a longitudinal pharmacy that is proactively looking out for patients.

Research areas

Related content

US, CA, San Francisco
Amazon has launched a new research lab in San Francisco to develop foundational capabilities for useful AI agents. We’re enabling practical AI to make our customers more productive, empowered, and fulfilled. Our work leverages large vision language models (VLMs) with reinforcement learning (RL) and world modeling to solve perception, reasoning, and planning to build useful enterprise agents. Our lab is a small, talent-dense team with the resources and scale of Amazon. Each team in the lab has the autonomy to move fast and the long-term commitment to pursue high-risk, high-payoff research. We’re entering an exciting new era where agents can redefine what AI makes possible. Key job responsibilities You will contribute directly to AI agent development in an applied research role to improve the multi-model perception and visual-reasoning abilities of our agent. Daily responsibilities including model training, dataset design, and pre- and post-training optimization. You will be hired as a Member of Technical Staff.
IN, TN, Chennai
Are you excited about the digital media revolution and passionate about designing and delivering advanced analytics that directly influence the product decisions of Amazon's digital businesses. Do you see yourself as a champion of innovating on behalf of the customer by turning data insights into action? The Amazon Digital Acceleration Analytics team is looking for an analytical and technically skilled individual to join our team. In this role, you will invent, build and deploy state of the art machine-learning models and systems to enable and enhance the team's mission This role offers wide scope, autonomy, and ownership. You will work closely with software engineers & data engineers to put algorithms into practice. You should have strong business judgement, excellent written and verbal communication skills. The candidate should be willing to take on challenging initiatives and be capable of working both independently and with others as a team. Key job responsibilities We are looking for an experienced data scientist with strong foundations in mathematics, statistics & machine learning with exceptional communication and leadership skills, and a proven track record of delivery. In this role, You will Define a long-term science vision and roadmap for the team, driven fundamentally from our customers' needs, translating those directions into specific plans for engineering teams. Design and execute machine learning projects/products end-to-end: from ideation, analysis, prototyping, development, metrics, and monitoring. Drive end-to-end statistical analysis that have a high degree of ambiguity, scale, and complexity. Research and develop advanced Generative AI based solutions to solve diverse customer problems. About the team The MIDAS team operates within Amazon's Digital Analytics (DA) engineering organization, building analytics and data engineering solutions that support cross-digital teams. Our platform delivers a wide range of capabilities, including metadata discovery, data lineage, customer segmentation, compliance automation, AI-driven data access through generative AI and LLMs, and advanced data quality monitoring. Today, more than 100 Amazon business and technology teams rely on MIDAS, with over 20,000 monthly active users leveraging our mission-critical tools to drive data-driven decisions at Amazon scale.
US, WA, Seattle
Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! We are forming a new organization within Prime Video to redefine our operational landscape through the power of artificial intelligence. As a Applied Scientist within this initiative, you will be a technical leader helping to design and build the intelligent systems that power our vision. You will tackle complex and ambiguous problems, designing and delivering scalable and resilient agentic AI and ML solutions from the ground up. You will not only write high-quality, maintainable software and models, but also mentor other scientists, influence our technical strategy, and drive engineering best practices across the team. Your work will directly contribute to making Prime Video's operations more efficient and will set the technical foundation for years to come. We're seeking candidates with strong experience in computer vision and generative AI technologies. In this role, you'll apply cutting-edge techniques in image and video understanding, visual content generation, and multimodal AI systems to transform how Prime Video operates at scale. Key job responsibilities • Lead the design and architecture of highly scalable, available, and resilient services for our AI automation platform. • Write high-quality, maintainable, and robust code to solve complex business problems, building flexible systems without over-engineering. • Act as a technical leader and mentor for other engineers on the team, assisting with career growth and encouraging excellence. • Work through ambiguous requirements, cut through complexity, and translate business needs into scalable technical solutions. • Take ownership of the full software development lifecycle, including design, testing, deployment, and operations. • Work closely with product managers, scientists, and other engineers to build and launch new features and systems. About the team This role offers a unique opportunity to shape the future of one of Amazon's most exciting businesses through the application of AI technologies. If you're passionate about leveraging AI to drive real-world impact at massive scale, we want to hear from you.
US, CA, San Francisco
Join the next revolution in robotics at Amazon's Frontier AI & Robotics team, where you'll work alongside world-renowned AI pioneers to push the boundaries of what's possible in robotic intelligence. As an Applied Scientist, you'll be at the forefront of developing breakthrough foundation models that enable robots to perceive, understand, and interact with the world in unprecedented ways. You'll drive independent research initiatives in areas such as perception, manipulation, science understanding, locomotion, manipulation, sim2real transfer, multi-modal foundation models and multi-task robot learning, designing novel frameworks that bridge the gap between state-of-the-art research and real-world deployment at Amazon scale. In this role, you'll balance innovative technical exploration with practical implementation, collaborating with platform teams to ensure your models and algorithms perform robustly in dynamic real-world environments. You'll have access to Amazon's vast computational resources, enabling you to tackle ambitious problems in areas like very large multi-modal robotic foundation models and efficient, promptable model architectures that can scale across diverse robotic applications. Key job responsibilities - Drive independent research initiatives across the robotics stack, including robotics foundation models, focusing on breakthrough approaches in perception, and manipulation, for example open-vocabulary panoptic scene understanding, scaling up multi-modal LLMs, sim2real/real2sim techniques, end-to-end vision-language-action models, efficient model inference, video tokenization - Design and implement novel deep learning architectures that push the boundaries of what robots can understand and accomplish - Lead full-stack robotics projects from conceptualization through deployment, taking a system-level approach that integrates hardware considerations with algorithmic development, ensuring robust performance in production environments - Collaborate with platform and hardware teams to ensure seamless integration across the entire robotics stack, optimizing and scaling models for real-world applications - Contribute to the team's technical strategy and help shape our approach to next-generation robotics challenges A day in the life - Design and implement novel foundation model architectures and innovative systems and algorithms, leveraging our extensive infrastructure to prototype and evaluate at scale - Collaborate with our world-class research team to solve complex technical challenges - Lead technical initiatives from conception to deployment, working closely with robotics engineers to integrate your solutions into production systems - Participate in technical discussions and brainstorming sessions with team leaders and fellow scientists - Leverage our massive compute cluster and extensive robotics infrastructure to rapidly prototype and validate new ideas - Transform theoretical insights into practical solutions that can handle the complexities of real-world robotics applications About the team At Frontier AI & Robotics, we're not just advancing robotics – we're reimagining it from the ground up. Our team is building the future of intelligent robotics through innovative foundation models and end-to-end learned systems. We tackle some of the most challenging problems in AI and robotics, from developing sophisticated perception systems to creating adaptive manipulation strategies that work in complex, real-world scenarios. What sets us apart is our unique combination of ambitious research vision and practical impact. We leverage Amazon's massive computational infrastructure and rich real-world datasets to train and deploy state-of-the-art foundation models. Our work spans the full spectrum of robotics intelligence – from multimodal perception using images, videos, and sensor data, to sophisticated manipulation strategies that can handle diverse real-world scenarios. We're building systems that don't just work in the lab, but scale to meet the demands of Amazon's global operations. Join us if you're excited about pushing the boundaries of what's possible in robotics, working with world-class researchers, and seeing your innovations deployed at unprecedented scale.
US, CA, San Francisco
Join the next revolution in robotics at Amazon's Frontier AI & Robotics team, where you'll work alongside world-renowned AI pioneers to push the boundaries of what's possible in robotic intelligence. As an Applied Scientist, you'll be at the forefront of developing breakthrough foundation models that enable robots to perceive, understand, and interact with the world in unprecedented ways. You'll drive independent research initiatives in areas such as perception, manipulation, science understanding, locomotion, manipulation, sim2real transfer, multi-modal foundation models and multi-task robot learning, designing novel frameworks that bridge the gap between state-of-the-art research and real-world deployment at Amazon scale. In this role, you'll balance innovative technical exploration with practical implementation, collaborating with platform teams to ensure your models and algorithms perform robustly in dynamic real-world environments. You'll have access to Amazon's vast computational resources, enabling you to tackle ambitious problems in areas like very large multi-modal robotic foundation models and efficient, promptable model architectures that can scale across diverse robotic applications. Key job responsibilities - Drive independent research initiatives across the robotics stack, including robotics foundation models, focusing on breakthrough approaches in perception, and manipulation, for example open-vocabulary panoptic scene understanding, scaling up multi-modal LLMs, sim2real/real2sim techniques, end-to-end vision-language-action models, efficient model inference, video tokenization - Design and implement novel deep learning architectures that push the boundaries of what robots can understand and accomplish - Lead full-stack robotics projects from conceptualization through deployment, taking a system-level approach that integrates hardware considerations with algorithmic development, ensuring robust performance in production environments - Collaborate with platform and hardware teams to ensure seamless integration across the entire robotics stack, optimizing and scaling models for real-world applications - Contribute to the team's technical strategy and help shape our approach to next-generation robotics challenges A day in the life - Design and implement novel foundation model architectures and innovative systems and algorithms, leveraging our extensive infrastructure to prototype and evaluate at scale - Collaborate with our world-class research team to solve complex technical challenges - Lead technical initiatives from conception to deployment, working closely with robotics engineers to integrate your solutions into production systems - Participate in technical discussions and brainstorming sessions with team leaders and fellow scientists - Leverage our massive compute cluster and extensive robotics infrastructure to rapidly prototype and validate new ideas - Transform theoretical insights into practical solutions that can handle the complexities of real-world robotics applications About the team At Frontier AI & Robotics, we're not just advancing robotics – we're reimagining it from the ground up. Our team is building the future of intelligent robotics through innovative foundation models and end-to-end learned systems. We tackle some of the most challenging problems in AI and robotics, from developing sophisticated perception systems to creating adaptive manipulation strategies that work in complex, real-world scenarios. What sets us apart is our unique combination of ambitious research vision and practical impact. We leverage Amazon's massive computational infrastructure and rich real-world datasets to train and deploy state-of-the-art foundation models. Our work spans the full spectrum of robotics intelligence – from multimodal perception using images, videos, and sensor data, to sophisticated manipulation strategies that can handle diverse real-world scenarios. We're building systems that don't just work in the lab, but scale to meet the demands of Amazon's global operations. Join us if you're excited about pushing the boundaries of what's possible in robotics, working with world-class researchers, and seeing your innovations deployed at unprecedented scale.
US, WA, Seattle
Are you excited to help customers discover the hottest and best reviewed products? The Discovery Tech team helps customers discover and engage with new, popular and relevant products across Amazon worldwide. We do this by combining technology, science, and innovation to build new customer-facing features and experiences alongside advanced tools for marketers. You will be responsible for creating and building critical services that automatically generate, target, and optimize Amazon’s cross-category marketing and merchandising. Through the enablement of intelligent marketing campaigns that leverage machine-learning models, you will help to deliver the best possible shopping experience for Amazon’s customers all over the globe. We are looking for analytical problem solvers who enjoy diving into data, excited about data science and statistics, can multi-task, and can credibly interface between engineering teams and business stakeholders. Your analytical abilities, business understanding, and technical savvy will be used to identify specific and actionable opportunities to solve existing business problems and look around corners for future opportunities. Your domain spans the design, development, testing, and deployment of data-driven and highly scalable machine learning solutions in product recommendation. As an Applied Scientist, you bring business and industry context to science and technology decisions. You set the standard for scientific excellence and make decisions that affect the way we build and integrate algorithms. Your solutions are exemplary in terms of algorithm design, clarity, model structure, efficiency, and extensibility. You tackle intrinsically hard problems, acquiring expertise as needed. You decompose complex problems into straightforward solutions. To know more about Amazon science, please visit https://www.amazon.science
ES, B, Barcelona
Are you a scientist passionate about advancing the frontiers of computer vision, machine learning, or large language models? Do you want to work on innovative research projects that lead to innovative products and scientific publications? Would you value access to extensive datasets? If you answer yes to any of these questions, you'll find a great fit at Amazon. We're seeking a hands-on researcher eager to derive, implement, and test the next generation of Generative AI, computer vision, ML, and NLP algorithms. Our research is innovative, multidisciplinary, and far-reaching. We aim to define, deploy, and publish pioneering research that pushes the boundaries of what's possible. To achieve our vision, we think big and tackle complex technological challenges at the forefront of our field. Where technology doesn't exist, we create it. Where it does, we adapt it to function at Amazon's scale. We need team members who are passionate, curious, and willing to learn continuously. Key job responsibilities * Derive novel computer vision and ML models or LLMs/VLMs. * Design and develop scalable ML models. * Create and work with large datasets * Work with large GPU clusters. * Work closely with software engineering teams to deploy your innovations. * Publish your work at major conferences/journals. * Mentor team members in the use of your AI models. A day in the life As a Senior Applied Scientist at Amazon, your typical day might look like this: * Dive into coding, deriving new ML models for computer vision or NLP * Experiment with massive datasets on our GPU clusters * Brainstorm with your team to solve complex AI challenges * Collaborate with engineers to turn your research into real products * Write up your findings for publication in top journals or conferences * Mentor junior team members on AI concepts and implementation About the team DiscoVision, a science unit within Amazon's UPMT, focuses on advancing visual content capabilities through state-of-the-art AI technology. Our team specializes in developing state-of-the-art technologies in text-to-image/video Generative AI, 3D modeling, and multimodal Large Language Models (LLMs).
US, WA, Seattle
We are seeking a Principal Applied Scientist to lead research and development in automated reasoning, formal verification, and program analysis. You will drive innovation in making formal methods practical and accessible for real-world systems at cloud scale. Key job responsibilities - Lead research initiatives in automated reasoning, formal verification, SMT solving, model checking, or program analysis - Design and implement novel algorithms and techniques that advance the state of the art - Mentor and guide applied scientists, research scientists, and engineers - Collaborate with product teams to transition research into production systems - Define technical vision and strategy for automated reasoning initiatives - Represent AWS in the academic and research community - Drive cross-organizational impact through technical leadership About the team The Automated Reasoning Group at AWS develops and applies cutting-edge formal methods and automated reasoning techniques to ensure the security, reliability, and correctness of AWS services and customer applications. Our work innovates tools and services to perform verification at scale and apply them to build safe and secure systems at AWS. We are also pioneering the use of formal verification and automated reasoning to develop agentic systems, ensuring AI agents operate within defined safety boundaries.
IN, TS, Hyderabad
Do you want to join an innovative team of scientists who leverage machine learning and statistical techniques to revolutionize how businesses discover and purchase products on Amazon? Are you passionate about building intelligent systems that understand and predict complex B2B customer needs? The Amazon Business team is looking for exceptional Applied Science to help shape the future of B2B commerce. Amazon Business is one of Amazon's fastest-growing initiatives focused on serving business customers, from individual professionals to large institutions, with unique and complex purchasing needs. Our customers require sophisticated solutions that go beyond traditional B2C experiences, including bulk purchasing, approval workflows, and business-grade service support. The AB-MSET Applied Science team focuses on building intelligent systems for delivering personalized, contextual service experiences throughout the customer lifecycle. We apply advanced machine learning techniques to develop sophisticated intent detection models for business customer service needs, create intelligent matching algorithms for optimal service routing based on multiple variables including customer value, maturity, effort, and issue complexity, build predictive models to enable proactive service interventions, design recommendation systems for self-service solutions, and develop ML models for automated service resolution. As an Applied Scientist on the team, you will design and develop state-of-the-art ML models for service intent classification, routing optimization, and customer experience personalization. You will analyze large-scale business customer interaction data to identify patterns and opportunities for automation, create scalable solutions for complex B2B service scenarios using advanced ML techniques, and work closely with engineering teams to implement and deploy models in production. You will collaborate with business stakeholders to identify opportunities for ML applications, establish automated processes for model development, validation, and maintenance, lead research initiatives to advance the state-of-the-art in B2B service science, and mentor other scientists and engineers in applying ML techniques to business problems.
US, WA, Bellevue
Amazon Leo is an initiative to increase global broadband access through a constellation of 3,236 satellites in low Earth orbit (LEO). Its mission is to bring fast, affordable broadband to unserved and underserved communities around the world. Amazon Leo will help close the digital divide by delivering fast, affordable broadband to a wide range of customers, including consumers, businesses, government agencies, and other organizations operating in places without reliable connectivity. Do you get excited by aerospace, space exploration, and/or satellites? Do you want to help build solutions at Amazon Leo to transform the space industry? If so, then we would love to talk! Key job responsibilities Work cross-functionally with product, business development, and various technical teams (engineering, science, simulations, etc.) to execute on the long-term vision, strategy, and architecture for the science-based global demand forecast. Design and deliver modern, flexible, scalable solutions to integrate data from a variety of sources and systems (both internal and external) and develop Bandwidth Usage models at granular temporal and geographic grains, deployable to Leo traffic management systems. Work closely with the capacity planning science team to ensure that demand forecasts feed seamlessly into their systems to deliver continuous optimization of resources. Lead short and long terms technical roadmap definition efforts to deliver solutions that meet business needs in pre-launch, early-launch, and mature business phases. Synthesize and communicate insights and recommendations to audiences of varying levels of technical sophistication to drive change across Amazon Leo. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum. About the team The Amazon Leo Global Demand Planning team's mission is to map customer demand across space and time. We enable Amazon Leo's long-term success by delivering actionable insights and scientific forecasts across geographies and customer segments to empower long range planning, capacity simulations, business strategy, and hardware manufacturing recommendations through scalable tools and durable mechanisms.