Careers

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

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.

The Ads Measurement Science team in the Measurement, Ad Tech, and Data Science (MADS) team of Amazon Ads serves a centralized role developing solutions for a multitude of performance measurement products. We create solutions which measure the comprehensive impact of their ad spend, including sales impacts both online and offline and across timescales, and provide actionable insights that enable our advertisers to optimize their media portfolios. We leverage a host of scientific technologies to accomplish this mission, including Generative AI, classical ML, Causal Inference, Natural Language Processing, and Computer Vision. We are hiring an Economist on the team to develop the next generation of incrementality measurement products, capturing the effect of advertising in driving sales as well as the effects of measurement tools on advertiser engagement with Amazon. As an Economist on the team, you will lead the design, implementation, and validation of large-scale causal inference methodologies to capture these properties. You will communicate your results with science and business leaders, and partner with other scientists and engineers to carry solutions into production. Key job responsibilities Leverage deep expertise in causal inference to develop robust, causally grounded ads measurement solutions Disambiguate problems to propose clear evaluation frameworks and success criteria Work autonomously and write high quality technical documents 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 and leaders Contribute new ideas that shape the direction of the team's work Mentor more junior scientists and participate in the hiring process

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 Ads Measurement Science team in the Measurement, Ad Tech, and Data Science (MADS) team of Amazon Ads serves a centralized role developing solutions for a multitude of performance measurement products. We create solutions which measure the comprehensive impact of advertiser's ad spend, including sales impacts both online and offline and across timescales, and provide actionable insights that enable our advertisers to optimize their media portfolios. We also own the science solutions for AI tools that unlock new insights and automate high-effort customer workflows, such as custom query and report generation based on natural language user requests. We leverage a host of scientific technologies to accomplish this mission, including Generative AI, classical ML, Causal Inference, Natural Language Processing, and Computer Vision. As an Applied Scientist on the team, you will lead measurement solutions end-to-end from inception to production. You will propose, design, analyze, and productionize models to provide novel measurement insights to our customers. Key job responsibilities Leverage deep expertise in one or more scientific disciplines to invent solutions to ambiguous ads measurement 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 more junior scientists and participate in the hiring process About the team We are a team of scientists across Applied, Research, Data Science and Economist disciplines. You will work with colleagues with deep expertise in ML, NLP, CV, 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.

In Amazon Advertising, we apply machine learning at massive scale to optimize the prediction, ranking, and bidding behind every ad — deciding, in milliseconds, which ads to show shoppers and how to value them. We're looking for an Applied Scientist to help make sure the ads shoppers see are the right ones for them. You'll work across the science of how we rank, value, and bid on ads for Amazon DSP (Amazon's Demand-Side Platform) — including how we judge whether an ad is a good fit for the page a shopper is on and for the shopper themselves. It's high-scale, low-latency, customer-facing science: your models run live in front of millions of shoppers under tight real-time constraints. The questions are genuinely open — how do you tell whether an ad is relevant to someone, how do you balance what's good for shoppers, advertisers, and Amazon, and how do you keep getting that right as shopping behavior and inventory shift underneath you? Your work will have real impact, and you'll have room to shape where we take it. A few things make this stand out: your models touch a huge share of the ads shoppers see every day, so even small improvements add up fast; you'll run modern ML live under strict latency limits, across regions and very different types of ad inventory; and the problem space is rich — from how we value and bid on ads, to keeping models stable as traffic shifts, to what makes an ad a good fit for a shopper. Key job responsibilities - Design and improve the models that decide how ads are ranked, valued, and priced — including how relevant an ad is to the page and the shopper. - Apply and extend state-of-the-art techniques across e.g. ranking, deep learning, and information retrieval. - Own problems end to end: frame them, prototype, experiment, and ship them to production. - Balance competing objectives — shopper experience, advertiser and publisher value, and Amazon's business — into models that hold up across placements and marketplaces. - Communicate your work clearly to both business and science audiences, tailoring how you share it to each. - Write and ship your own production code backed by strong engineering support — we're all builders here. - Move fast with the best tools available, including modern AI coding assistants and agents. A day in the life You might start by digging into last week's experiment results, then use an AI coding agent to get your next prototype built and ready to test in production. In the afternoon you could be sketching a new way to measure ad relevance, reading a recent paper that bears on it, and talking it through with a senior scientist on the team. You'll move between hands-on science, writing and shipping real production code, and making the calls on your own work. About the team We're a group of scientists and engineers based in Edinburgh and London, working to make Amazon's ads more performant and relevant. We sit within a larger team spread primarily across New York City and the UK, and we have a broad mandate to build and experiment. You'll work alongside senior applied scientists you can learn from, with the data and infrastructure to do the work well and room to grow — with opportunities to attend top conferences (e.g., NeurIPS, KDD, ICML) and take on more scope over time.

Join our Amazon Private Brands Selection Guidance organization in building science and tech solutions at scale to delight our customers with products across our leading private brands such as Amazon Basics, Amazon Essentials, and by Amazon. The Selection Guidance team applies Generative AI, Machine Learning, Statistics, and Economics solutions to drive our private brands product assortment, strategic business decisions, and product inputs such as title, price, merchandising and ordering. We are an interdisciplinary team of Scientists, Economists, Engineers, and Product Managers incubating and building day one solutions using novel technology, to solve some of the toughest business problems at Amazon. As a Data Scientist you will investigate business problems using data, invent novel solutions and prototypes, and directly contribute to bringing your ideas to life through production implementation. Current research areas include named entity recognition, product substitutes, pricing optimization, agentic AI, and large language models. You will review and guide scientists across the team on their designs and implementations, and raise the team bar for science research and prototypes. This is a unique, high visibility opportunity for someone who wants to develop ambitious science solutions and have direct business and customer impact. Key job responsibilities - Partner with business stakeholders to deeply understand APB business problems and frame ambiguous business problems as science problems and solutions. - Perform data analysis and build data pipelines to drive business decisions. - Invent novel science solutions, develop prototypes, and deploy production software to solve business problems. - Review and guide science solutions across the team. - Publish and socialize your and the team's research across Amazon and external avenues as appropriate - Leverage industry best practices to establish repeatable applied science practices, principles & processes.

The Central Learning Solutions (CLS) Science team in Amazon Operations builds state-of-the-art solutions for enhancing leadership and associate development across the organization. We develop technology and mechanisms for building personalized learning experiences based on the profiles of different learners and assess the post-training performance curves for different learner profiles. As a Data Scientist on the team, you will be driving the data science roadmap for the CLS Science team. You will leverage your knowledge in statistics and econometrics, estimate the causal impact of training interventions, recommend the right interventions for a given learner profile, and measure the post-launch success of these interventions through A/B weblabs. These insights will help in dynamically changing the training content of Learning & Development courses and unlock opportunities to improve both training effectiveness and learner experience. You will collaborate effectively with internal stakeholders and cross-functional teams for solving business problems, create operational efficiencies, and deliver successfully against high organizational standards. Key job responsibilities - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and implementation. - Use advanced causal inference methodologies to estimate the learning curves for different learner profiles and the effectiveness of training content. - Perform statistical analysis and statistical tests including hypothesis testing and A/B testing. - Implement new statistical, machine learning, or other mathematical methodologies to solve specific business problems. - Present deep dives and analysis to both technical and non-technical stakeholders, ensure clarity, and influence the strategy of business partners. A day in the life 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: - Medical, Dental, and Vision Coverage - Maternity and Parental Leave Options - Paid Time Off (PTO) - 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 Central Learning Solutions (CLS) is the centralized technical team that builds Learning & Development products for North America Operations employees. CLS Science teams owns the science components that power the different products on Impact Estimation, Self Serve Analytics, Content Understanding, and Personal learning.

The Ads Trust Science team, based in Bangalore, is responsible for ensuring that ads are relevant and is of good quality, leading to higher conversion for the sellers and providing a great experience for the customers. We deal with one of the world’s largest product catalog, handle billions of requests a day with plans to grow it by order of magnitude and use automated systems to validate tens of millions of offers submitted by thousands of merchants in multiple countries and languages. In this role, you will build and develop ML models to address content understanding problems in Ads. These models will rely on a variety of visual and textual features requiring expertise in both domains. These models need to scale to multiple languages and countries. You will collaborate with engineers and other scientists to build, train and deploy these models. As part of these activities, you will develop production level code that enables moderation of millions of ads submitted each day.

Do you want to transform the way people shop at Amazon? We are looking for stellar applied scientists to be part of our multi-disciplinary team. We are re-imagining the future of the Amazon shopping experience for customers through tailoring it to their current intent. We understand what customers are interested in shopping for, and guide them to discover what they need as they shop. If you love building new technology that helps customers solve their problems, this is your opportunity to impact millions of customers. Come help us build the future of personalized shopping at Amazon! As an Applied Scientist on the team, you will work on science innovation in our space across a large multidisciplinary team. You will have a breadth of problems to work on, ranging from developing state of the art LLM-based techniques to reason about customers and products, developing deep learned transformer-based models to understand and abstract customer intent signals and representations, building large-scale real-time multi-task ranking systems, and more. You will build technology employed by teams across the company, while also having a direct connection to millions of customers through our own customer facing features every day. Come join us in the journey! Key job responsibilities Key job responsibilities * Deliver new features and models that have huge impact on the customer experience. Help customers find the right products and content on their shopping journey. Leverage the use of advanced machine learning to create customer shopping experience at Amazon's scale - for all Amazon customers across all countries in realtime * Be a key contributor on a multidisciplinary team across science, product, design, and engineering to see through ideas from inception, prototype, to launch in the hands of all Amazon's customers * Propose and innovate on the science roadmap across multiple projects About the team Our vision is to build the next generation of shopping experience at Amazon through personalization and understanding the customer's intent. We imagine our core experiences to work together as a talented personal shopping assistant would — a partner that is knowledgeable, understands your preferences, and helps you find the right solution for your needs. We aim for the quality of personalization to be a core reason customers choose Amazon, on par with Earth’s largest selection, low prices, and fast and free shipping. Our team is part of Amazon’s Personalization organization, a high-performing group that leverages Amazon’s expertise in machine learning, big data, distributed systems, and user experience design to deliver the best shopping experiences for our customers. We run global experiments and our work has revolutionized e-commerce with features such as "Keep shopping for ...", “Customers who bought this item also bought”, and “Frequently bought together”. Amazon’s internal surveys regularly recognize us as one of the best engineering organizations to work for in the company, with visible high-impact work, low operational load, respectful work-life balance, and continual opportunity to learn and grow.