Careers

External job description As a Sr. Applied Scientist, you will be responsible for assessing and optimizing the performance and reliability of our new and emerging category of devices – Kuiper Customer Terminal. Project Kuiper is an initiative to launch a constellation of Low Earth Orbit satellites that will provide low-latency, high-speed broadband connectivity to un-served and under-served communities around the world. In this role, you will use expertise in physical sciences, theoretical, numerical or empirical techniques to create scalable models representing response of physical systems or devices, including: - Applying domain scientific expertise towards developing innovative analysis and tests to study viability of new materials, designs or processes - Working closely with engineering teams to drive validation, optimization and implementation of hardware design or software algorithmic solutions to improve product and customer risks - Establishing scalable, efficient, automated processes to handle large scale design and data analysis - Conducting research into use conditions, materials and analysis techniques - Tracking general business activity including device health in field and providing clear, compelling reports to management on a regular basis - Developing, implementing guidelines to continually optimize design processes - Using simulation tools like LS-DYNA, and Abaqus for analysis and optimization of product design - Using of programming languages like Python and Matlab for analytical/statistical analyses and automation - Demonstrating strong understanding across multiple physical science domains, e.g. structural, thermal, fluid dynamics, and materials - Developing, analyzing and testing structural solutions from concept design, feature development, product architecture, through system validation - Supporting product development and optimization through application of analysis and testing of complex electronic assemblies using advanced simulation and experimentation tools and techniques

Are you excited by the idea of developing personalized experiences for Amazon customers as they shop? Are you looking create a huge impact as you help build a state-of-the-art recommendation system? Join us and you'll help millions of customers make informed purchase decisions while also advancing the state of Amazon's science through your research! Key job responsibilities - Participate in the design, development, evaluation, deployment and updating of data-driven models for shopping personalization. - Apply supervised and uplift learning techniques to improve ML performance - Research and implement ML and statistical approaches to add value to the business. - Design A/B tests and conduct statistical analysis on their results - Apply machine learning and statistical algorithms to harness enormous volumes of data as we serve our customers - Work closely with internal stakeholders like the business teams, engineering teams and partner teams and align them with respect to your focus area - Present science research, contributing to Amazon's science community - Mentor junior engineers and scientists. A day in the life As a Senior Data Scientist in the MAPLE team, your day might start with a stand-up meeting, aligning priorities with your colleagues. You'll then dive into analyzing the results of a recent A/B test on a new recommendation algorithm you've developed. Midday, you might collaborate with engineers to optimize the implementation of your model for production. In the afternoon, you could find yourself mentoring a junior team member on statistical techniques or presenting your latest findings to business stakeholders. You'll also dedicate time to staying current with the latest research in machine learning and recommendation systems, possibly contributing to an internal tech talk or external publication. Throughout the day, you'll be using your expertise to solve complex problems, turning data into actionable insights that enhance the customer experience on Amazon's platform. About the team Our team's mission is to surface the right payments-related recommendations to customers at the right time, helping create a rewarding and successful shopping experience for Amazon's customers. Our team's culture is highly collaborative, with an emphasis on supporting each other and learning from one another. We dedicate time each week to focus on personal development and expanding our knowledge as a team. We also highly value having a big impact, both for Amazon's business and for our customers.

Do you want to lead the Ads industry and redefine how we measure the effectiveness of the WW Amazon Ads business? Are you passionate about causal inference, raising the science bar, and connecting leading-edge science research to Amazon-scale implementation? If so, come join Amazon Ads to lead our Advertising Incrementality Measurement science team! Key job responsibilities As the leader of Advertising Incrementality Measurement (AIM) science team, you are responsible for defining and executing on our causal measurement science vision. You will lead teams of Applied Scientists, Economists, and Data Scientists to work backwards from customer needs and translate product ideas into concrete deliverables. You will be the thought leader for inventing scalable causal measurement solutions that support highly accurate and actionable causal insights--from defining and executing hundreds of thousands of RCTs, to building and scaling Deep Neural Nets and other leading ML models. You will solve hard problems, advance science at Amazon, and be the leading innovator in the causal measurement of advertising effectiveness. In this role, you will work with a team of applied scientists, economists, engineers, product managers, and UX designers to define and build the future of advertising causal measurement. You will be working with massive data, a dedicated engineering team, and industry-leading partner scientists. Your team’s work will help shape the future of Amazon Advertising.

The Artificial General Intelligence (AGI) team is looking for a highly skilled and experienced Senior Applied Scientist, to lead the development and implementation of cutting-edge algorithms and models for supervised fine-tuning, reinforcement learning through human feedback and complex reasoning; with a focus across text, image, and video modalities. As a Senior Applied Scientist, you will play a critical role in driving the development of Generative AI (GenAI) technologies that can handle Amazon-scale use cases and have a significant impact on our customers' experiences. Key job responsibilities - Collaborate with cross-functional teams of engineers, product managers, and scientists to identify and solve complex problems in GenAI - Design and execute experiments to evaluate the performance of different algorithms and models, and iterate quickly to improve results - Think big about the arc of development of GenAI over a multi-year horizon, and identify new opportunities to apply these technologies to solve real-world problems - Communicate results and insights to both technical and non-technical audiences, including through presentations and written reports - Mentor and guide junior scientists and engineers, and contribute to the overall growth and development of the team

Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. We offer experiences that serve all listeners with our different tiers of service: Prime members get access to all the music in shuffle mode, and top ad-free podcasts, included with their membership; customers can upgrade to Amazon Music Unlimited for unlimited, on-demand access to 100 million songs, including millions in HD, Ultra HD, and spatial audio; and anyone can listen for free by downloading the Amazon Music app or via Alexa-enabled devices. Join us for the opportunity to influence how Amazon Music engages fans, artists, and creators on a global scale. Key job responsibilities - Work backwards from customer problems to research and develop novel machine learning solutions for music and podcast recommendations. Through A/B testing and online experiments done hand-in-hand with engineering teams, you'll implement and validate your ideas and solutions. - Advocate solutions and communicate results, insights and recommendations to stakeholders and partners. - Produce innovative research on recommender systems that shapes the field and meets the high standards of peer-reviewed publications. You'll cement your team's reputation as thought leaders pioneering new recommenders. Stay current with advancements in the field, adapting latest in literature to build efficient and scalable models A day in the life Lead innovation in ML to shape Amazon Music experiences for millions. Collaborate with talented engineers and scientists to guide research and build scalable models across our audio portfolio - music, podcasts, live streaming, and more. Drive experiments and rapid prototyping, leveraging Amazon's data at scale. Innovate daily alongside world-class teams to delight customers worldwide through personalization. About the team The team is responsible for models that underly all of Amazon Music’s recommendations across content types on mobile, web and Alexa. You will collaborate with a team of product managers, applied scientists and software engineers delivering meaningful recommendations, personalized for each of the millions of customers using Amazon Music globally. As a scientist on the team, you will be involved in every aspect of the development lifecycle, from idea generation and scientific research to development and deployment of advanced models. You will work closely with engineering to realize your scientific vision.

The Planning and Execution team (PLEX) is looking for a Data Scientist to provide analytical support to planning and execution activities throughout North America. PLEX is comprised of high-powered dynamic teams, which are shaping network execution through the development and application of innovative labor & flow planning mechanisms. Our goal is to improve and enhance the Amazon Fulfillment network to ultimately drive the best customer experience in a reliable and cost-efficient manner that is truly world-class. Creating reliable & scalable forecasting techniques requires a sound understanding of the data and creation of quality input for ML models . Your data analytics will influence the performance of all North America Amazon Fulfillment networks. You will be working on large-scale data-sets and leverage your knowledge in advanced data analysis, ML, statistics, and other scientific tools to provide detailed insights that help scientists in the team to create accurate time-series forecasting models and other value-added research to the business. You will contribute in building scalable scientific models & algorithms and provide visualizations to drive data insight. You will own & drive the strategic insights and recommendations for the business that plays a key role in labor planning. We are looking for a passionate scientist with a commitment to teamwork. Successful candidates will have a deep knowledge of statistical and data analysis techniques to examine complex massive data. You will have the communication skills necessary to impact and influence leadership & partner teams through technical writings, presentations and discussions. You will learn a lot, grow, and have fun in the process! Innovation Opportunities & Career Growth Our business grows fast and we want our employees growing with it too. We provide constant opportunities for growth in our team through regular training, talent development, mentoring, and mechanisms conducive to incubating ideas from the bottom up to showcase your innovations. Inclusive Team Culture Here at Amazon, we promote an inclusive and engaging environment. We understand the strength that unique experiences bring to the team and value it. In our team, we uphold that all individuals should feel included, respected, and developed. Flexibility It's not the hours that you put into work matters, rather it's the quality of work that you put in. We provide flexibility and support to help you find a balance between your work and personal lives. This position will be based in Austin, TX We are open to hiring candidates to work out of one of the following locations: - Austin, TX - Bellevue, WA - Nashville, TN - New York, NY Key job responsibilities - Design and implement advanced forecasting models to optimize fulfillment network operations - Develop scalable ML models and algorithms for time-series forecasting - Analyze large-scale complex data to make decisions in scientific research projects - Create visualization tools to provide detailed insights into the data and end-to-end systems that drive strategic decisions in the business - Lead & partner with research, data engineering and product teams to perform mathematical & statistical research for labor & flow planning and optimization - Simplify the scientific methodology and data analysis by navigating through the technology complexities, explaining them in plain customer and business context to our partners & customers. - Lead & partner with software and product teams to support productization and integration of mathematical models and enhanced logic into portal-based tools About the team PLEX-SIA (Science, Intelligence, & Analytics) serves as the research, automation, and insight arm of the Planning & Execution team of NA Supply Chain org. PLEX-SIA identifies technical gaps, analytical opportunities, and complex operational and planning trends within NACF, in order to action them strategically and sustainably. PLEX-SIA Science team is the science wing of PLEX-SIA that specifically focuses on the creation, improvement, and automation of labor planning models and processes. This is achieved through AI and ML modeling, scientific analysis of existing processes, and optimization techniques. Science team partners with tech & non-tech partners to improve existing tech solutions and provide data driven recommendations for strategic model implementations.

This position gives you an opportunity to build metrics that shape Amazon's catalog initiatives world wide. If that rings a bell and if you possess the confidence to navigate through early stage ambiguities, read on. Amazon Selection and Catalog Systems (ASCS) builds the systems that host and run the world’s largest e-Commerce products catalog - it powers the online buying experience for customers worldwide so they can find, discover and buy anything they want. Amazon’s customers rely on the completeness, consistency and correctness of Amazon's product data to make well-informed purchase decisions. Improving the quality of product data is a continuous process. It requires data driven decisions on what product data changes simplify and improve the Customers’ experience. Our team seeks a Sr. Applied Scientist with demonstrated experience in experimentation techniques and causal inference at scale. Our problems include attributing values to actions in complex world of catalog information driving customer behavior. The ideal candidate combines acumen in data science and causal modeling to grapple with these and other challenges and guide decision-making at the highest levels. This is an opportunity to influence catalog quality improvements across Amazon. Key job responsibilities 1. Build models to attribute customer impact to specific LLM generated product data quality improvements. You will need high judgment for balancing cost efficiency of your models with accuracy of the estimates. 2. Partner with Product Managers and Engineering to build and scale new customer experience metrics 3. Build new business metrics in the A/B experimentation platform 4. Guide quality improvement programs by generating actionable insights About the team We enable teams across Amazon to run A/B experiments on product listings through Catalog Experimentation Program. Additionally, using experimentation and causal inference models, we build customer impact metrics for different experiences in Amazon stores world wide. We help Catalog data quality initiatives understand the customer impact of their work streams and influence their priorities to maximize customer benefits.

Are you looking for an opportunity to build an LLM-based enterprise-grade, highly available, large scale solution? Does it excite you to find patterns and build generic, composable solutions to solve complex problems? Are you looking for inventing newer and simpler ways of building solutions? If so, we are looking for you to fill a challenging position in Alexa Enterprise (AE) team. AE brings the power of Alexa voice assistant to enterprise partners in industries such as hospitality and senior living. We tackle pressing challenges like workforce shortage. We are inventing Large Language Models (LLM)-driven interactions to create memorable moments for users while simultaneously boosting partner revenues and reinforcing brand identity. Beyond managed properties, AE extends Alexa's reach to premium third-party devices, seamlessly integrating with household names like Samsung, LG, and Sonos, thus amplifying its impact across diverse ecosystems. AE team is looking for a passionate, highly skilled and inventive Senior Applied Scientist, with a strong machine learning background, to lead the development and implementation of state-of-the-art ML systems for Alexa Enterprise use cases. As a Senior Applied Scientist in the team, you will play a critical role in driving the development of conversational assistants, in particular those based on Large Language Models (LLM's), that meet enterprise standards. You will handle Amazon-scale use cases with significant impact on our customers' experiences. Key job responsibilities - You will analyze, understand and improve user experiences by leveraging Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in artificial intelligence - You will work on core LLM technologies, including developing best-in-class modeling, prompt optimization algorithms to enable Conversation AI use cases - Build and measure novel online & offline metrics for personal digital assistants and customer scenarios, on diverse devices and endpoints - Create, innovate, and deliver deep learning, policy-based learning, and/or machine learning-based algorithms to deliver customer-impacting results - Perform model/data analysis and monitor metrics through online A/B testing

AWS Payments is seeking an Applied Scientist to drive high-impact science initiatives to help mitigate financial losses, create frictionless payment experience, minimize the cost of payment processing, and prevent abuses/exploitations of payment systems by bad actors. As an Applied Scientist within AWS Payments organization, your role is to leverage your strong background in Data Science and Machine Learning to build best-in-class payment risk assessment frameworks that enable efficient, data-driven decisions anytime, anywhere across payment lifecycle. You will analyze rich datasets at Amazon scale and provide insights to improve existing AI/ML solutions as well as drive new scientific initiatives that enhance the payments experience of millions of customers. This role requires a pragmatic technical leader who is comfortable navigating ambiguous environments and is capable of effectively summarizing complex data analysis and modeling results through clear verbal explanations and written documentations. The ideal candidate will have experience with machine learning models and applying science to various business contexts, especially experience in dealing with payments or financial services data. You will have to work with a group of other scientists, product managers and engineers and play an integral role in strategic decision-making. The right candidate will possess excellent business and communication skills, define business objectives and prioritize work across the team to support business outcomes, and develop solutions to key business problems. Key job responsibilities - Interact with product managers, business teams, and engineering teams to develop an understanding and domain knowledge of business requirements, processes and system structures. - Apply domain knowledge and business judgment to identify opportunities and quantify the impact aligning research direction to business requirements and make the right judgment on research project prioritization. - Develop scalable mathematical models to derive optimal or near-optimal solutions to existing and new challenges in the AWS payments space. - Improve upon existing methodologies by integrating new data sources, developing new models or algorithmic enhancements and fine-tuning model parameters. - Advocate technical solutions to business stakeholders, engineering teams, as well as executive level decision makers. - Work closely with engineers to integrate prototypes into production systems. - Frame evaluation methods to monitor the actual performance of devised solutions in production systems, identify areas with potential for improvement and work with internal teams to improve the solution with new features. - Lead the project plan from a scientific perspective on product launches including identifying key milestones, potential risks and paths to mitigate risks. About the team 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. 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. 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. 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.

You will collaborate cross-functionally with engineering teams and program managers throughout the organization to deliver projects aimed at characterizing and improving the performance of new robotic automation technology in Amazon's network of warehouses. In this role, you will use a combination of unified and disparate data sources to uncover insights delivered through decision-driving analytics white-papers and automated data visualizations to influence the development and deployment of new robotic technologies. You will collaborate with Data Engineers, Software, Hardware, and Program Managers to define and deliver world class robotics technologies, analytics tools, and insights to shape the robotic technology landscape. We are looking for an enthusiastic data science professional with a passion for delivering business influencing analysis, papers, and recommendations to complex and ambiguous problems. A practiced data scientists, who uses data engineering, statistics, data visualization, and data science to influence decision making in cross-functional organizations. We are seeking an individual who can think holistically through compound problems to understand how systems work together to define and execute projects which drive improvements to robotic architecture or design. A day in the life In this role, you'll support new technologies looking for decision-making support and deep root cause analysis. These technologies will be in alpha/beta phases and have a start-up mentality of heavy Bias for Action, Ownership, and Customer Obsession. Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply! About the team The Amazon Robotics Storage Analytics team discovers insights in vast and varied robotic data to help our engineering teams understand how their technologies operate across Amazon’s network of fulfillment warehouses. We collaborate with designers, software and hardware engineers, and operations teams to understand product requirements, make feature trade-offs, design, and operate new applications of Amazon Robotics Storage technology. We conduct relevant, insightful analysis and communicate the results through white papers and presentations. Our methods are inclusive of design of experiments, statistical modeling, machine learning, financial analysis, and data visualization.