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

IN, KA, Bangalore
Have you ever ordered a product on Amazon and when that box with the smile arrived you wondered how it got to you so fast? Have you wondered where it came from and how much it cost Amazon to deliver it to you? If so, the WW Amazon Logistics, Business Analytics team is for you. We manage the delivery of tens of millions of products every week to Amazon’s customers, achieving on-time delivery in a cost-effective manner. We are looking for an enthusiastic, customer obsessed, Sr. Applied Scientist with good analytical skills to help manage projects and operations, implement scheduling solutions, improve metrics, and develop scalable processes and tools. The primary role of an Operations Research Scientist within Amazon is to address business challenges through building a compelling case, and using data to influence change across the organization. This individual will be given responsibility on their first day to own those business challenges and the autonomy to think strategically and make data driven decisions. Decisions and tools made in this role will have significant impact to the customer experience, as it will have a major impact on how the final phase of delivery is done at Amazon. Ideal candidates will be a high potential, strategic and analytic graduate with a PhD in (Operations Research, Statistics, Engineering, and Supply Chain) ready for challenging opportunities in the core of our world class operations space. Great candidates have a history of operations research, and the ability to use data and research to make changes. This role requires robust program management skills and research science skills in order to act on research outcomes. This individual will need to be able to work with a team, but also be comfortable making decisions independently, in what is often times an ambiguous environment. Responsibilities may include: - Develop input and assumptions based preexisting models to estimate the costs and savings opportunities associated with varying levels of network growth and operations - Creating metrics to measure business performance, identify root causes and trends, and prescribe action plans - Managing multiple projects simultaneously - Working with technology teams and product managers to develop new tools and systems to support the growth of the business - Communicating with and supporting various internal stakeholders and external audiences
US, NY, New York
We are seeking an Applied Scientist to lead the development of evaluation frameworks and data collection protocols for robotic capabilities. In this role, you will focus on designing how we measure, stress-test, and improve robot behavior across a wide range of real-world tasks. Your work will play a critical role in shaping how policies are validated and how high-quality datasets are generated to accelerate system performance. You will operate at the intersection of robotics, machine learning, and human-in-the-loop systems, building the infrastructure and methodologies that connect teleoperation, evaluation, and learning. This includes developing evaluation policies, defining task structures, and contributing to operator-facing interfaces that enable scalable and reliable data collection. The ideal candidate is highly experimental, systems-oriented, and comfortable working across software, robotics, and data pipelines, with a strong focus on turning ambiguous capability goals into measurable and actionable evaluation systems. Key job responsibilities - Design and implement evaluation frameworks to measure robot capabilities across structured tasks, edge cases, and real-world scenarios - Develop task definitions, success criteria, and benchmarking methodologies that enable consistent and reproducible evaluation of policies - Create and refine data collection protocols that generate high-quality, task-relevant datasets aligned with model development needs - Build and iterate on teleoperation workflows and operator interfaces to support efficient, reliable, and scalable data collection - Analyze evaluation results and collected data to identify performance gaps, failure modes, and opportunities for targeted data collection - Collaborate with engineering teams to integrate evaluation tooling, logging systems, and data pipelines into the broader robotics stack - Stay current with advances in robotics, evaluation methodologies, and human-in-the-loop learning to continuously improve internal approaches - Lead technical projects from conception through production deployment - Mentor junior scientists and engineers About the team Fauna Robotics, an Amazon company, is building capable, safe, and genuinely delightful robots for everyday life. Our goal is simple: make robots people actually want to live and interact with in everyday human spaces. We believe that future won’t arrive until building for robotics becomes far more accessible. Today, too much effort is spent reinventing the fundamentals. We’re changing that by developing tightly integrated hardware and software systems that make it faster, safer, and more intuitive to create real-world robotic products. Our work spans the full stack: mechanical design, control systems, dynamic modeling, and intelligent software. The focus is not just functionality, but experience. We’re building robots that feel responsive, expressive, and genuinely useful. At Fauna, you’ll work at the frontier of this space, helping define how robots move, manipulate, and interact with people in natural environments. It’s an opportunity to solve hard problems across hardware and software with a team focused on making robotics accessible and joyful to build. If you care about making robotics real for everyone and building systems that are as delightful as they are capable, we’re interested in hearing from you.
US, CA, Sunnyvale
Industrial is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine innovative AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. We leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. We are pioneering the development of robotics foundation models that: - Enable unprecedented generalization across diverse tasks - Integrate multi-modal learning capabilities (visual, tactile, linguistic) - Accelerate skill acquisition through demonstration learning - Enhance robotic perception and environmental understanding - Streamline development processes through reusable capabilities The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. As an Applied Scientist, you will develop and improve machine learning systems that help robots perceive, reason, and act in real-world environments. You will leverage state-of-the-art models (open source and internal research), evaluate them on representative tasks, and adapt/optimize them to meet robustness, safety, and performance needs. You will invent new algorithms where gaps exist. You’ll collaborate closely with research, controls, hardware, and product-facing teams, and your outputs will be used by downstream teams to further customize and deploy on specific robot embodiments. Key job responsibilities As an Applied Scientist in the Foundations Model team, you will: - Leverage state-of-the-art models for targeted tasks, environments, and robot embodiments through fine-tuning and optimization. - Execute rapid, rigorous experimentation with reproducible results and solid engineering practices, closing the gap between sim and real environments. - Build and run capability evaluations/benchmarks to clearly profile performance, generalization, and failure modes. - Contribute to the data and training workflow: collection/curation, dataset quality/provenance, and repeatable training recipes. - Write clean, maintainable, well commented and documented code, contribute to training infrastructure, create tools for model evaluation and testing, and implement necessary APIs - Stay current with latest developments in foundation models and robotics, assist in literature reviews and research documentation, prepare technical reports and presentations, and contribute to research discussions and brainstorming sessions. - Work closely with senior scientists, engineers, and leaders across multiple teams, participate in knowledge sharing, support integration efforts with robotics hardware teams, and help document best practices and methodologies.
US, CA, Palo Alto
About Sponsored Products and Brands The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. Key job responsibilities As a Machine Learning Applied Scientist, you will: * Conduct deep data analysis to derive insights to the business, and identify gaps and new opportunities * Develop scalable and effective machine-learning models and optimization strategies to solve business problems * Run regular A/B experiments, gather data, and perform statistical analysis * Work closely with software engineers to deliver end-to-end solutions into production * Improve the scalability, efficiency and automation of large-scale data analytics, model training, deployment and serving * Conduct research on new machine-learning modeling and Generative AI solutions to optimize all aspects of Sponsored Products and Brands business About the team The Ad Response Prediction team within Sponsored Products and Brands (SPB) drives personalized shopping experiences for SPB Ads across placements, pages, and devices worldwide. We achieve this through ML and GenAI solutions that include customized shopper response prediction and session-level understanding to optimize every stage of the ad-serving process, from sourcing and bidding to widget discovery and auctions. Our responsibilities include advancing response prediction through model and feature innovations and extending prediction beyond the auction stage to areas such as targeting, sourcing, and bidding.
US, NY, New York
We are seeking a Measurement and Attribution Analytics scientist to further the development and application of analytics methods to examine the complex data flows of measurement, attribution and shopper analytics 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 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. - 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-dives A day in the life The Measurement and Attribution 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. There will be an emphasis on understanding the impact of shopper journeys to and from Amazon properties and their implication on product development. The candidate will then analyze the data and present papers and ongoing reports on actionable insights. About the team The 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.
IN, KA, Bangalore
Does the thought of improving one of the world’s most complex logistic systems inspire you? Is your passion to sift through hundreds of systems, processes, and data sources to solve the puzzle and identify the next big opportunity? Are you a creative big thinker who is passionate about using data to direct decision making and solve complex and large-scale challenges? Are you fascinated by the interactions between operations and strategy? Do you feel like your skills uniquely qualify you to bridge communication between teams with competing priorities? If so, then this position is for you! Come help Amazon create state-of-the-art science-driven technologies for delivering packages to the doorstep of our customers! The Last Mile Routing & Planning organization builds the software, algorithms and tools that make the “magic” of home delivery happen: our flow, sort, dispatch and routing intelligence systems are responsible for the billions of daily decisions needed to plan and execute safe, efficient and frustration-free routes for drivers around the world. Our team supports deliveries (and pickups!) for Amazon Logistics, Same Day, Amazon Grocery, Lockers, and other new initiatives across the world. Key job responsibilities In this role, your main focus will be to apply algorithms, synthesize information, identify business opportunities, provide data-driven insights and communicate business and technical requirements within the team and across stakeholder groups. You will partner closely with other scientists and engineers in a collegial environment with a clear path to business impact. We have an exciting portfolio of research areas including vehicle routing, planning for electric and autonomous vehicles, district and stops planning, ultra-fast deliveries, fleet planning, and forecasting solutions for different delivery programs leveraging the latest OR, ML, and Generative AI methods, at a global scale. Successful candidates will have a deep knowledge of Operations Research and/or Machine/Deep Learning methods, experience in applying these methods to large-scale business problems, the ability to map models into production-worthy code in Python or Java, the communication skills necessary to explain complex technical approaches to a variety of stakeholders and customers, and the excitement to take iterative approaches to tackle big research challenges.
US, CA, San Francisco
Innovators wanted! Are you an entrepreneur? A builder? A dreamer? This role is part of an Amazon Special Projects team that takes the company’s Think Big leadership principle to the extreme. We focus on creating entirely new products and services with a goal of positively impacting the lives of our customers. No industries or subject areas are out of bounds. If you’re interested in innovating at scale to address big challenges in the world, this is the team for you. Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have thirteen employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We are constantly learning through programs that are local, regional, and global. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Our team highly values work-life balance, mentorship and career growth. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We care about your career growth and strive to assign projects and offer training that will challenge you to become your best.
US, WA, Seattle
About Sponsored Products and Brands The Sponsored Products and Brands (SPB) team at Amazon Ads is re-imagining the advertising landscape through generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. About our team The General Shopping Intelligence (GSI) team is a highly motivated, collaborative, and fun-loving group with a strong entrepreneurial spirit and bias for action. We provide advanced real-time machine learning services that connect shoppers with the right ads across all platforms and surfaces worldwide. Through deep understanding of both shoppers and products, we help shoppers discover new products they love, enable advertisers to reach their customers most efficiently, and help Amazon continuously innovate on behalf of all customers. We are seeking a motivated Applied Scientist who loves to innovate at the intersection of customer experience, deep learning, generative AI and high-scale machine learning systems. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. Key job responsibilities As an Applied Scientist, you will: * Leverage Generative AI and Large Language Models (LLMs) to mine complex behavioral data, deriving deep, actionable shopper insights that identify customer experience gaps and unlock new business opportunities. * Design and develop scalable machine learning and GenAI models focused on shopper intent and preference modeling, ensuring a rapid path from prototype to production. * Partner closely with engineering teams to architect and deploy end-to-end GenAI solutions into production, integrating advanced insights directly into real-time, customer-facing systems. * Drive the scalability, efficiency, and automation of large-scale model training and real-time inference systems, pioneering the LLM infrastructure required to support next-generation GenAI workloads at Amazon Ads scale. * Design and run rigorous A/B experiments to quantify the business and customer impact of GenAI-driven shopper insights, performing advanced statistical analysis to guide iterative production rollouts. * Conduct applied research in novel generative AI techniques (e.g., fine-tuning, RAG, agentic workflows) to optimize the shopper experience and drive performance across all aspects of the Sponsored Products and Brands business.
US, WA, Bellevue
As an Applied Scientist in Amazon Fullfilment Technology, you will lead the development of agentic systems to assist with operational decision making and orchestration. You will work building full agentic systems leveraging multi-agent orchestration, tool use, memory, and action execution. You will train LLMs using a combination of rejection sampling approaches, SFT, continual post-training, and Reinforcement Learning (RL). These systems are deployed to Amazon buildings, and you will also work on rigorous offline and online evaluations. Your work will leverage the latest LLMs to develop capabilities for agentic reasoning, coding and analytics. You will also lead research projects to tackle unsolved problems, mentor interns, and author academic papers to summarize your findings for external publication. Key job responsibilities - Generating training and preference data for specific use cases (reasoning trajectories, tool traces) - Reward modeling and policy optimization for LLMs: DPO, IPO, RLHF/RLAIF with PPO/GRPO, rejection sampling. - Supervised fine-tuning on step-by-step trajectories and tool-use traces - Verbal Reinforcement Learning and Continual Learning - RL for LLMs, Offline RL and off-policy evaluation - Agentic memory/state management; episodic and semantic memory; vector search; grounding with RAG. - Evaluation: developing decision quality metrics, scaling LLM-based evaluations. About the team Amazon Fulfillment Technologies (AFT) powers Amazon's global fulfillment network. We invent and deliver software, hardware, and data science solutions that orchestrate processes, robots, machines, and people. We harmonize the physical and virtual world so Amazon customers can get what they want, when they want it. Learn more about AFT: https://tinyurl.com/AFTOverview
US, VA, Arlington
Are you excited about making business decisions using science and data? Are you interested in supporting consumer device concepts from idea inception to launch? Do you want to work on a Science Product team focused on scaling statistics and econometrics with custom tools? If so, this may be the role for you! Amazon.com strives to be Earth's most customer-centric company. The Amazon Devices and Services team focuses on delighting customer by enabling seamless functionality in supplying, entertaining, and managing the home -- and beyond. We seek and hire the world's brightest minds, offering them a fast-paced, technologically-sophisticated, and friendly work environment, where economic theory meets real-world industry. The Decision Science team in Devices owns demand estimates and pricing recommendations of concept devices before customers know they exist. We support devices and services ranging from Echo Frames to Kindle Paperwhite to Blink Video Camera …all prior to launch. We are a cross-functional Product team working to scale Econometrics through Amazon and beyond by incorporating Science into internal facing tools and making it easier for others to do so as well. In this role, you will have input in decision meetings with Amazon senior leadership, which include go/no-go decisions for brand new devices and services and build volume decisions for manufacture prior to receiving any customer signal. You will have direct input to pricing decisions. You will leverage Science and Tools produced by the Decision Science team such as conjoint demand models to produce these recommendations. You will work with Scientists, Economists, Product Managers, and Software Developers to provide meaningful feedback about stakeholder problems to inform business solutions and increase the velocity, quality, and scope behind our recommendations. You will also have the opportunity to work on special projects to both guide the business and advance your own knowledge and understanding of specific topics. Key job responsibilities Applies expertise to develop econometric/machine learning models to measure the demand of devices and the business; Reviews models and results for other scientists, mentors junior scientists; Generates economic insights for the Devices and Services business and work with stakeholders to run the business for effectively; Describes strategic importance of vision inside and outside of team; and, Identifies business opportunities, defines the problem and how to solve it; Engages with senior scientists, business leadership outside Devices and Services to understand interplay between different business units.