Careers

Amazon DynamoDB is a fully managed NoSQL database that serves more than 1 million customers and delivers single-digit millisecond performance at any scale. It supports individual tables over 200TB and sustains over half a million requests per second for hundreds of customers, with up to 99.999% availability. Behind that scale sits a large fleet of capacity that must be placed and balanced continuously. We are looking for an Applied Scientist to advance the science of capacity utilization and data placement across the DynamoDB fleet. You will work backwards from customer experience and fleet economics to find where capacity is used inefficiently, where scaling bottlenecks constrain the service, and where smarter data placement can raise utilization without degrading latency or availability. You will turn these findings into models and algorithms that inform capacity profile decisions and placement policy. You will partner closely with the DynamoDB teams to bring your inputs into production decisions. This is a customer-obsessed science role for a self-driven scientist. Many of the problems are not yet well defined and no textbook solution exists. You will frame the problem, extend state-of-the-art approaches or invent new ones, and drive the work to production impact with a strong bias for action. Key job responsibilities Identify capacity usage optimization opportunities across the DynamoDB fleet. Quantify the customer and cost impact of each opportunity. - Model the scaling bottlenecks of the service and characterize how they constrain placement and utilization. - Develop data placement approaches that balance customer experience (latency, availability, throughput headroom) against optimal capacity utilization. - Partner with the DynamoDB performance team to incorporate your inputs into capacity profile decisions and placement policy. Validate impact with production data. - Build components that integrate directly into production systems or that directly support the large systems making placement and capacity decisions. - Scrutinize the performance of your algorithms and software during implementation. Resolve root causes and leave systems easier to maintain. - Author or co-author papers for internal or external peer-reviewed venues when the work is novel and business considerations allow.

MULTIPLE POSITIONS AVAILABLE Employer: AMAZON DEVELOPMENT CENTER U.S., INC. Offered Position: Applied Scientist III Job Location: New York, New York Job Number: AMZ10165497 Position Responsibilities: Participate in the design, development, evaluation, deployment and updating of data-driven models and analytical solutions for machine learning (ML) and/or natural language (NL) applications. Develop and/or apply statistical modeling techniques (e.g. Bayesian models and deep neural networks), optimization methods, and other ML techniques to different applications in business and engineering. Routinely build and deploy ML models on available data, and run and analyze experiments in a production environment. Identify new opportunities for research in order to meet business goals. Research and implement novel ML and statistical approaches to add value to the business. Mentor junior engineers and scientists. Position Requirements: Master’s degree or foreign equivalent degree in Computer Science, Machine Learning, Engineering, or a related field and two years of research or work experience in the job offered, or as a Research Scientist, Research Assistant, Software Engineer, or a related occupation. Employer will accept a Bachelor’s degree or foreign equivalent degree in Computer Science, Machine Learning, Engineering, or a related field and five years of progressive post-baccalaureate research or work experience in the job offered or a related occupation as equivalent to the Master’s degree and two years of research or work experience. Must have one year of research or work experience in the following skill(s): (1) programming in Java, C++, Python, or equivalent programming language; and (2) conducting the analysis and development of various supervised and unsupervised machine learning models for moderately complex projects in business, science, or engineering. Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation. 40 hours / week, 8:00am-5:00pm, Salary Range $183,800/year to $248,700/year. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, visit: https://www.aboutamazon.com/workplace/employee-benefits.#0000

Amazon.com’s Product Detail Page team is looking for talented, motivated and passionate applied scientist to be part of the design and development of a highly scalable multi-tiered shopping application to provide the best possible online shopping experience for Amazon customers world-wide. Our team is comprised of talented applied scientists, developers, testers, program managers, designers and product managers tasked with the singular goal to create THE world's best buying experience. Scientists on this team develop the next-generation technologies and experiences that change how millions interact and shop online. To provide the best possible online shopping at the scale of the web requires ideas from every area of computer science, including distributed computing, large-scale system design, machine learning, natural language processing, data compression and user interface design; the list goes on and is growing every day. We need our scientists to be versatile and always eager to tackle new problems as we continue to push technology forward. Our team leverages sophisticated econometric, machine learning, and big data technologies to help customers to discover the right products at the right prices from millions of trusted sellers billions of times a day. If you are looking for a career-defining opportunity on one of the most customer centric and business impacting teams within Amazon, we’d love to hear from you. We are looking for an Applied Scientist to help build the next generation of Detail Page optimization algorithms. These new set of algorithms will incorporate the continually changing preferences of our customers and continue to scale with numerous new programs that Amazon is introducing for our customers. You will work with multiple Amazon businesses and programs to identify big business opportunities and propose new business features and technical systems to improve customer experience on Amazon Detail Page, Search Page and many other widgets throughout the website. You will be responsible for the quality of algorithm design and will get the opportunity to present your ideas and share results of your deliverables with Amazon executives on a frequent basis. You will get an opportunity to work with senior scientists to define and enforce broad, company-wide technical standards in optimization techniques, statistical modeling and simulation techniques, and/or data analytics.

The People eXperience and Technology Central Science (PXTCS) team uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, well-being, and the value of work to Amazonians. The Benefits Science team is looking for an economist to transform complex business challenges into actionable scientific insights. In this role, you will partner directly with business leaders to design and evaluate pilots, build models using large-scale data, and scale successful prototypes into company-wide policies and programs. We're looking for someone who can combine rigorous scientific thinking with practical business acumen and is passionate about using economics to improve employee experiences at scale. The ideal candidate will thrive in interdisciplinary environments, working alongside engineers, data scientists, and business leaders from diverse backgrounds. Key job responsibilities - Design and conduct rigorous evaluations of benefits programs - Support the development and application of structural models - Develop experiments to evaluate the impact of benefits initiatives - Communicate complex findings to business stakeholders in clear, actionable terms - Work with engineering teams to develop scalable tools that automate and streamline evaluation processes A day in the life Work with teammates to apply economic methods to business problems. This might include identifying the appropriate research questions, writing code to implement a DID analysis or estimate a structural model, or writing and presenting a document with findings to business leaders. Our economists also collaborate with partner teams throughout the process, from understanding their challenges, to developing a research agenda that will address those challenges, to help them implement solutions.

The Sponsored Products and Brands (SPB) team at Amazon Ads is re-imagining the advertising landscape through state-of-the-art 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 This role will be pivotal in redesigning how ads contribute to a personalized, relevant, and inspirational shopping experience, with the customer value proposition at the forefront. Key responsibilities include, but are not limited to: - Contribute to the design and development of GenAI, deep learning, multi-objective optimization and/or reinforcement learning empowered solutions to transform ad retrieval, auctions, whole-page relevance, and/or bespoke shopping experiences. - Collaborate cross-functionally with other scientists, engineers, and product managers to bring scalable, production-ready science solutions to life. - Stay abreast of industry trends in GenAI, LLMs, and related disciplines, bringing fresh and innovative concepts, ideas, and prototypes to the organization. - Contribute to the enhancement of team’s scientific and technical rigor by identifying and implementing best-in-class algorithms, methodologies, and infrastructure that enable rapid experimentation and scaling. - Mentor and grow junior scientists and engineers, cultivating a high-performing, collaborative, and intellectually curious team. A day in the life As an Applied Scientist on the Sponsored Products and Brands Off-Search team, you will contribute to the development in Generative AI (GenAI) and Large Language Models (LLMs) to revolutionize our advertising flow, backend optimization, and frontend shopping experiences. This is a rare opportunity to redefine how ads are retrieved, allocated, and/or experienced—elevating them into personalized, contextually aware, and inspiring components of the customer journey. You will have the opportunity to fundamentally transform areas such as ad retrieval, ad allocation, whole-page relevance, and differentiated recommendations through the lens of GenAI. By building novel generative models grounded in both Amazon’s rich data and the world’s collective knowledge, your work will shape how customers engage with ads, discover products, and make purchasing decisions. If you are passionate about applying frontier AI to real-world problems with massive scale and impact, this is your opportunity to define the next chapter of advertising science. About the team The Off-Search team within Sponsored Products and Brands (SPB) is focused on building delightful ad experiences across various surfaces beyond Search on Amazon—such as product detail pages, the homepage, and store-in-store pages—to drive monetization. Our vision is to deliver highly personalized, context-aware advertising that adapts to individual shopper preferences, scales across diverse page types, remains relevant to seasonal and event-driven moments, and integrates seamlessly with organic recommendations such as new arrivals, basket-building content, and fast-delivery options. To execute this vision, we work in close partnership with Amazon Stores stakeholders to lead the expansion and growth of advertising across Amazon-owned and -operated pages beyond Search. We operate full stack—from backend ads-retail edge services, ads retrieval, and ad auctions to shopper-facing experiences—all designed to deliver meaningful value. Curious about our advertising solutions? Discover more about Sponsored Products and Sponsored Brands to see how we’re helping businesses grow on Amazon.com and beyond!

We are seeking an Applied Science Manager to lead the Cost-to-Serve science team within JCI MOST (Measurement and Optimization Science Team). This team builds the causal models, optimization systems, and AI-driven analytics that power Amazon Japan's CtS program — identifying where cost saving opportunities exist across the supply chain, explaining why they exist, and quantifying their dollar impact. You will manage a team of applied scientists, economists, and data scientists working across causal inference, consolidation optimization, supply chain forecasting, and GenAI-powered analytics. Your team's work directly shapes how VP-level leadership makes investment decisions across CtS levers in Amazon Japan. Key Responsibilities -Lead and grow a team of scientists delivering causal models, optimization engines, and AI-driven insights for supply chain cost reduction -Set the science roadmap and prioritize across workstreams: causal attribution, financial simulation, forecasting, and GenAI agent development -Partner with product, engineering, operations, and finance to translate science into operational impact -Drive the integration of science models into AI tools — making causal reasoning accessible to non-technical stakeholders at scale -Represent CtS science to VP-level leadership through MBR/QBR mechanisms and OP planning At Amazon, you'll work alongside the latest AI and GenAI tools that are increasingly woven into how teams operate: from AI-powered capabilities that accelerate decision-making, to Generative AI that helps you focus on work that truly matters. You'll have opportunities and resources to develop AI fluency at your own pace, with continuous learning built into the culture.

The Media Planning Science team develops and implements models that deliver insights and recommendations for strategic media planning and measurement across Amazon Advertising's product portfolio. Our mission is to help advertisers create and execute plans that meet their objectives while providing accurate measurement tools. We work on a multitude of problem statements that encompass Incremental Reach, Budget Planning Optimization, and Recommendations. Our models leverage both heuristic and machine learning approaches including deep learning techniques, with insights delivered through agent-based tools and APIs that integrate seamlessly into user interfaces and programmatic systems to ensure optimal advertising outcomes. As an Applied Scientist on the team, you will be at the forefront of innovation, developing media planning solutions end-to-end from inception to production. You will propose, design, analyze, and productionize models to provide novel insights to our customers. Key job responsibilities * Leverage deep expertise in one or more scientific disciplines to invent solutions to ambiguous ads measurement and media planning problems * Disambiguate problems to propose clear evaluation frameworks and success criteria * Work autonomously and write high quality technical documents * Implement a significant portion of critical-path code, and partner with engineers to directly carry solutions into production * Partner closely with other scientists to deliver large, multi-faceted technical projects * Share and publish works with the broader scientific community through meetings and conferences * Communicate clearly to both technical and non-technical audiences * Contribute new ideas that shape the direction of the team's work * Mentor junior scientists and participate in the hiring process A day in the life You will solve real-world problems by analyzing large amounts of data, generate business insights and opportunities, design simulations and experiments, and develop ML/DL models. The team is driven by business needs, which requires collaboration with other Scientists, Engineers, and Product Managers across the advertising organization. You will prepare written and verbal documents to share insights to audiences of varying levels of technical sophistication. About the team We are a team of scientists across Applied and Data Science disciplines. You will work with colleagues with deep expertise in ML, DL, NLP, Gen AI, and Causal Inference with a diverse range of backgrounds. We partner closely with top-notch engineers, product managers, sales leaders, and other scientists with expertise in the ads industry and on building scalable modeling and software solutions.

The OTS ANCHOR (Analytics, Insights, and Centralized Hub for OTS Reporting) team is part of the WW IT Operations & RME organization, responsible for delivering data solutions that drive operational excellence across Amazon's global IT infrastructure. We build data pipelines, predictive models, develop analytical intelligence frameworks, and create AI-powered solutions that transform how Amazon manages, triages, and resolves IT operational incidents at scale pointing towards the Letting Data Tell the Story north start vision. We are seeking a Senior Data Scientist to own the end-to-end data science charter for critical operational workstreams — from forecasting IT service demand and capacity planning to designing intelligent severity triaging models and building the analytical backbone for agentic AI solutions. You will work backwards from complex operational problems to create models and solutions that directly impact the efficiency and reliability of Amazon's IT operations worldwide. Key job responsibilities - Design, develop, and deploy machine learning models for IT operations forecasting, including incident volume prediction, capacity planning, and workforce demand modeling - Build and maintain severity classification and intelligent triaging models that process thousands of daily IT service tickets across global regions - Develop anomaly detection systems to identify emerging patterns in operational data, enabling proactive incident prevention - Create econometric and statistical frameworks to measure the impact of operational interventions and optimize resource allocation decisions - Partner with engineering teams to productionize ML models via automated pipelines using AWS services (SageMaker, Step Functions, Lambda, Redshift) - Design and conduct experiments to empirically validate model performance and operational impact - Develop data quality monitoring frameworks and establish standards for operational data integrity - Collaborate with cross-functional teams (ServiceNow platform, Decision Intelligence, Field Operations) to translate complex analytical insights into actionable strategies - Communicate findings, model performance, and strategic recommendations to senior leaders through written documents and presentations - Mentor junior data scientists and engineers on the team; raise the bar on scientific rigor and analytical best practices - Contribute to the team's multi-year science roadmap, identifying new opportunities to apply ML/AI (including Generative AI and agentic architectures) to operational challenges A day in the life Amazon Benefits: 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

The Alexa Daily Essentials team delivers experiences critical to how customers interact with Alexa as part of daily life. Alexa users engage with our products across experiences connected to Timers, Alarms, Calendars, Food, and News. Our experiences include critical time saving techniques, ad-supported news audio and video, and in-depth kitchen guidance aimed at serving the needs of the family from sunset to sundown. As a Data Scientist on our team, you'll work with complex data, develop statistical methodologies, and provide critical product insights that shape how we build and optimize our solutions. You will work closely with your Analytics and Applied Science teammates. You will build frameworks and mechanisms to scale data solutions across our organization. If you are passionate about redefining how AI can improves everyone's daily life, we’d love to hear from you. Key job responsibilities Key job responsibilities Problem-Solving - Analyze complex data to identify patterns, inform product decisions, and understand root causes of anomalies. - Develop analysis and modeling approaches to drive product and engineering actions to identify patterns, insights, and understand root causes of anomalies. Your solutions directly improve the customer experience. - Independently work with product partners to identify problems and opportunities. Apply a range of data science techniques and tools to solve these problems. Use data driven insights to inform product development. Work with cross-disciplinary teams to mechanize your solution into scalable and automated frameworks. Data Infrastructure - Build data pipelines, and identify novel data sources to leverage in analytical work - both from within Alexa and from cross Amazon - Acquire data by building the necessary SQL / ETL queries Communication - Excel at communicating complex ideas to technical and non-technical audiences. - Build relationships with stakeholders and counterparts. Work with stakeholders to translate causal insights into actionable recommendations - Force multiply the work of the team with data visualizations, presentations, and/or dashboards to drive awareness and adoption of data assets and product insights - Collaborate with cross-functional teams. Mentor teammates to foster a culture of continuous learning and development

About us As part of the AWS Applied AI Solutions organization, our vision is to provide business applications, leveraging Amazon’s unique experience and expertise, that are used by millions of companies worldwide to manage day-to-day operations. We will accomplish this by accelerating our customers’ businesses through delivery of intuitive and differentiated technology solutions that solve enduring business challenges. Our team combines Amazon's real-world experience with state-of-art AI to create opinionated, turnkey solutions that are no-brainers to buy and easy to use. We're building applied AI solutions that businesses love and trust. Our ambition is to become the partner companies rely on to run their business every day—putting AI to work to deliver better customer experiences, operational excellence, and faster innovation. We're a fast-moving, scrappy team building a new agentic product from the ground up. If bias for action is your favorite leadership principle, you'll fit right in. The Role We're seeking a talented Senior Applied Scientist with expertise in large language models, agentic systems, and foundational models. You will be responsible for building the state-of-art multi-agent system, using a handful of methods including fine-tunning, reinforcement learning, etc. You'll accelerate our customer-facing features, contribute to our collaborative and innovative culture, and bring state-of-art applied research that raises the bar for the entire team. Key job responsibilities • Drive end-to-end GenAI projects with high complexity and ambiguity from conception to production • Build, optimize, and deploy ML models while collaborating with software engineers for productionization • Research innovative machine learning approaches and identify new opportunities for GenAI applications • Perform hands-on analysis and modeling of large datasets to develop actionable insights • Establish scalable, automated processes for data analysis, model development, and validation • Present results to senior leadership and collaborate with cross-functional teams About the team Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.