Careers

Are you interested in shaping the future of Advertising and B2B Sales? We are a growing team with an exciting AI-first charter and need your passion, innovative thinking, and creativity to help take our products to new heights. Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising products that drive discovery and sales. Our products are strategically important to our businesses driving long term growth. We break fresh ground in product and technical innovations every day! Within the Advertising Sales organization, we are building a central AI/ML team and are seeking top Applied Science talent to help us build new, science-backed services that drive success for our customers. Our goal is to transform the way account teams operate by creating actionable insights and recommendations they can share with their advertising accounts, and ingesting Generative AI throughout their end-to-end workflows to improve their work efficiency. As an Applied Scientist on the team, you will bring deep expertise in quantitative modeling techniques such as Sequential Recommender Systems, Deep Learning, Reinforcement Learning or Hidden Markov Models. You have the scientific and technical skills to build and refine models that can be implemented in production, and you leverage Natural Language Processing and Generative AI models to enhance their explainability. You will contribute to chart new courses with our ad sales support technologies, and you have the communication skills necessary to explain complex technical approaches to a variety of stakeholders and customers. You will be part of a team of fellow scientists and engineers taking on iterative approaches to tackle big, long-term problems. You are fluently able to leverage the latest Generative AI systems and services to accelerate and improve your work while maintaining high quality in your work outputs. Key job responsibilities Scientific Modeling - Conceptualize and lead state-of-the-art research on new Machine Learning and Generative Artificial Intelligence solutions to optimize all aspects of the Ad Sales business - Lead the technical approach for the design and implementation of successful models and algorithms in support of expert cross-functional teams delivering on demanding projects - Run regular A/B experiments, gather data, and perform statistical analysis - Improve the scalability, efficiency and automation of large-scale data analytics, model training, deployment and serving - Publish scientific findings in reports and papers that can be shared internally and externally Product Development Support - Partner with software engineering and product management teams to support product and service development, define success metrics and measurement approaches, and help drive adoption of innovative new features for our services. - Lead requirements gathering sessions with product teams and business stakeholders - Maintain scientific documentation and knowledge for product initiatives Collaboration & Communication - Work closely with software engineers to deliver end-to-end solutions into production - Translate complex scientific findings into actionable business recommendations for stakeholders and senior management - Provide clear, compelling reports and presentations on a regular basis with respect to your models and services - Communicate with internal teams to showcase results and identify best practices. About the team Sales AI is a central science and engineering organization within Amazon Advertising Sales that powers selling motions and account team workflows via state-of-the-art of AI/ML services. Sales AI is investing in a range of sales intelligence models, including the development of advertiser insights, recommendations and Generative AI-powered applications throughout account team workflows.

As part of the AWS Applied AI Solutions organization, we're building the future of AI-powered enterprise services across multiple domains. Our vision is to be the trusted foundation for transforming every business with Amazon AI teammates. Our mission is to deliver turnkey, enterprise-grade foundational AI capabilities that create delightful AI powered solutions. We're developing sophisticated AI systems that address complex challenges across autonomous operations, geospatial intelligence, trust and safety, IoT services, and foundational AI platforms. Key job responsibilities * Develop and productize AI solutions that address complex technical challenges requiring novel approaches beyond off-the-shelf tools * Design and implement machine learning systems for diverse applications including video understanding, geospatial optimization, fraud detection, anomaly detection, and automation * Create scalable algorithms and models that generalize across multiple customer use cases and business problems * Conduct rigorous experimentation with state-of-the-art techniques including large language models, computer vision, federated learning, or physics-based modeling, and agentic AI systems * Collaborate with engineering teams to integrate science components into production systems with measurable customer impact * Work directly with product teams to understand requirements, frame ambiguous problems into tractable science solutions, and validate approaches through proof of concepts * Establish evaluation frameworks and best practices for measuring solution performance and business impact * Mentor other scientists and contribute to the broader scientific community through publications when appropriate A day in the life As an Applied Scientist, you'll work on challenging problems that span multiple domains within AWS Core Services. You might develop video processing architectures for autonomous systems, create optimization solvers for geospatial applications, build behavioral detection models for fraud prevention, design anomaly detection systems for IoT devices, or develop specialized AI capabilities for platform services. You'll investigate novel approaches, validate ideas through rigorous experimentation with real data, and collaborate with scientists and engineers to transform research insights into scalable solutions. About the team Our team is a central science organization supporting multiple product teams across AWS Core Services. We tackle fundamental challenges in AI and machine learning that require novel approaches beyond off-the-shelf solutions. Working at the intersection of machine learning, large language models, and domain-specific applications, we develop practical techniques that advance the state-of-the-art while maintaining a clear path to customer impact. Our team builds deep domain expertise across geospatial intelligence, trust and safety systems, autonomous operations, and other critical areas, collaborating closely with engineering teams to transform research insights into scalable production solutions.

AWS Global Services includes experts from across AWS who help our customers design, build, operate, and secure their cloud environments. Customers innovate with AWS Professional Services, upskill with AWS Training and Certification, optimize with AWS Support and Managed Services, and meet objectives with AWS Security Assurance Services. Our expertise and emerging technologies include AWS Partners, AWS Sovereign Cloud, AWS International Product, and the Generative AI Innovation Center. You’ll join a diverse team of technical experts in dozens of countries who help customers achieve more with the AWS cloud. Do you have proven analytical capabilities to identify business opportunities, develop predictive models and optimization algorithms to help us build state of the art Support organization? At Amazon, we are working to be the most customer-centric company on earth. To get there, we need exceptionally talented, bright, and driven people. We set big goals and are looking for people who can help us reach and exceed them. Amazon Web Services (AWS) is one of the world’s most comprehensive and broadly adopted cloud platform, offering over 200 fully featured services from data centers globally. Amazon Web Services, Inc. provides services for broad range of applications including compute, storage, databases, networking, analytics, machine learning and artificial intelligence (AI), Internet of Things (IoT), security, and application development, deployment, and management. Global AWS central support team is looking for a passionate Data Scientist to model contact forecasting, discovering insights and identifying opportunities through the use of statistics, machine learning, and deep learning to drive business and operational improvements. A successful candidate must be passionate about building solutions that will help drive a more efficient operations network and optimize cost. In this role, you will partner with data engineering, Tooling team, operations, Training, Customer Service, Capacity planning and finance teams, driving optimization and prediction solutions across the network. Key job responsibilities We are looking for an experienced and motivated Data Scientist with proven abilities to build and manage modeling projects, identify data requirements, build methodology and tools that are statistically grounded The candidate will be an expert in the areas of data science, optimization, machine learning and statistics, and is comfortable facilitating ideation and working from concept through execution. The candidate is customer obsessed, innovative, independent, results-oriented and enjoys working in a fast-paced growing organization. An interest in operations, manufacturing or process improvement is helpful. The ability to embrace this ambiguity and work with a highly distributed team of experts is critical. As we scale up, there is opportunity to own globally impactful work and grow your career in technical, programmatic or people leadership. You will likely work with Python or R, though specific particular modelling language. Your problem-solving ability, knowledge of data models and ability to drive results through ambiguity are more important to us. 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 below, 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 in the cloud.

Are you interested in shaping the future of Advertising and B2B Sales? We are a growing science and engineering team with an exciting charter and need your passion, innovative thinking, and creativity to help take our products to new heights. Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising products that drive discovery and sales. Our products are strategically important to our businesses driving long term growth. We break fresh ground in product and technical innovations every day! Within the Advertising Sales organization, we are building a central AI/ML team and are seeking top science talent to build new, science-backed services to drive success for our customers. Our goal is to transform the way account teams operate by creating actionable insights and recommendations they can share with their advertising accounts, and ingesting Generative AI throughout their end-to-end workflows to improve their work efficiency. As a part of our team, you will bring deep expertise in Generative AI and quantitative modeling (forecasting, recommender systems, reinforcement learning, causal inferencing or generative artificial intelligence) to build and refine models that can be implemented in production. You will contribute to chart new courses with our ad sales support technologies, and you have the communication skills necessary to explain complex technical approaches to a variety of stakeholders and customers. You will be part of a team of fellow scientists and engineers taking on iterative approaches to tackle big, long-term problems. Why you will love this opportunity: Amazon has invested heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon's Retail and Marketplace businesses. We deliver billions of ads impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit with a broad mandate to experiment and innovate. Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences; this is your opportunity to work within the fastest growing businesses across all of Amazon! Define a long-term scientific vision for our advertising sales business, driven from our customers' needs, translating that direction into specific plans for scientists, engineers and product teams. This role combines scientific leadership, organizational ability, technical strength, product focus, and business understanding. Key job responsibilities - Conceptualize and lead state-of-the-art research on new Machine Learning and Generative Artificial Intelligence solutions to optimize all aspects of the Ad Sales business - Guide the technical approach for the design and implementation of successful models and algorithms in support of expert cross-functional teams delivering on demanding projects - Conduct deep data analysis to derive insights to the business, and identify gaps and new opportunities - 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 About the team Sales AI is a central science and engineering organization within Amazon Advertising Sales that powers selling motions and account team workflows via state-of-the-art of AI/ML services. Sales AI is investing in a range of sales intelligence models, including the development of advertiser insights, recommendations and Generative AI-powered applications throughout account team workflows.

As part of the AWS Applied AI Solutions organization, we're advancing the frontier of trust and safety systems for cloud-based communication services. Our vision is to be the trusted foundation for transforming every business with Amazon AI teammates. Our mission is to deliver turnkey, enterprise-grade foundational AI capabilities that create delightful AI powered solutions. We're building sophisticated AI systems that protect infrastructure from evolving threats while enabling legitimate high-volume users to operate without friction, with messaging services at scale as a key application area. Key job responsibilities - Develop advanced machine learning approaches and agentic systems that autonomously adapt to evolving threat patterns across cloud communication services - Create behavioral detection models that quickly identify malicious patterns after onboarding rather than creating friction during signup - Design intelligent resource allocation algorithms that optimize service delivery based on real-time feedback - Develop frameworks operating at scale across diverse usage patterns, analyzing hundreds of thousands of daily active customers - Research novel approaches combining AI agents with trust and safety systems to solve complex security problems - Collaborate with engineering teams to integrate science components into production systems - Conduct rigorous experimentation and establish evaluation frameworks to measure solution performance A day in the life As an Applied Scientist, you'll develop fraud detection algorithms and AI-powered security systems while maintaining a clear path to customer impact. You'll investigate novel approaches to behavioral analysis, develop methods for real-time reputation assessment, and validate ideas through rigorous experimentation. You'll collaborate with other scientists and engineers to transform research insights into scalable solutions, work directly with enterprise customers to understand requirements, and help shape the future of cloud security technology. About the team Our team is a central science organization supporting multiple product teams across AWS Core Services. We tackle fundamental challenges in AI and machine learning that require novel approaches beyond off-the-shelf solutions. Working at the intersection of machine learning, large language models, and domain-specific applications, we develop practical techniques that advance the state-of-the-art while maintaining a clear path to customer impact. Our team builds deep domain expertise across geospatial intelligence, trust and safety systems, autonomous operations, and other critical areas, collaborating closely with engineering teams to transform research insights into scalable production solutions.

Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering AI-powered solutions that transform how advertisers make strategic decisions. We deliver billions of ad impressions and process massive volumes of advertiser data every single day. You'll work with us to pioneer breakthrough approaches in how AI agents access and reason over real-time advertiser data at scale. We are using generative AI and agentic systems to help advertising agents provide instant, strategic advice to millions of advertisers. You will need to invent new techniques for agent orchestration, context optimization, and code generation to ensure we're delivering accurate, trustworthy insights with minimal latency and token consumption. You'll create feedback loops to ensure our solutions are constantly evaluating themselves and improving. The Ads Real-Time Data Service team is seeking an exceptional Applied Scientist to research and develop novel approaches for agent-data interaction. The Ads Real-Time Data Service team is solving one of the most critical challenges in advertising AI: instant access to advertiser context. We're building the infrastructure that provides immediate, pre-computed access to advertiser data via Model Context Protocol (MCP) servers—an emerging standard for AI agent-data interaction. We're building summarized data for context using a mix of state of the art techniques like CodeAct and RAG-based embeddings, achieving a fundamental transformation in how AI agents interact with data. This role balances applied research (60%) with productionization (40%), giving you the opportunity to both advance the state of the art and see your innovations deployed at Amazon scale. Key job responsibilities Agent Orchestration & Optimization Research - Research and develop novel algorithms for agent-data interaction patterns that minimize latency, token consumption, and error rates - Design and implement CodeAct pattern variations enabling agents to write and execute analytical code in isolated sandboxes - Investigate multi-agent orchestration strategies for complex advertiser queries requiring data from multiple sources - Develop techniques for automatic query optimization and caching strategies based on agent behavior patterns Large Language Model Context & Token Optimization - Invent new methods for compressing advertiser context representations while preserving semantic meaning and analytical utility - Research optimal metadata generation techniques that help large language models understand and reason over structured advertiser data - Design experiments to measure the impact of different data representations on agent response quality and token efficiency - Develop adaptive context selection algorithms that dynamically choose relevant data based on query intent RAG-Based Embeddings & Semantic Search - Pioneer new RAG-based embedding approaches optimized for real-time advertiser data delivery with sub-second latency - Research and implement semantic search and retrieval techniques for advertiser datasets using vector embeddings - Design advertiser context frameworks that enable automatic schema mapping from advertiser concepts to data representations - Develop evaluation frameworks to measure performance across dimensions of latency, accuracy, and developer experience Experimentation & Productionization - Design and execute rigorous experiments comparing traditional API orchestration versus CodeAct patterns and RAG-based approaches across metrics like success rate, latency, token consumption, and response quality - Analyze large-scale advertiser interaction data to identify patterns, bottlenecks, and optimization opportunities - Collaborate with engineering teams to productionize research innovations and deploy them to advertising agents and skills - Establish evaluation metrics and benchmarks for agent-data interaction performance A day in the life You start your morning analyzing experiment results from overnight runs comparing three evaluations for different RAG-based embedding approaches. The data shows that one of the embedding pattern is returning a significant improvement in accuracy. You create a spec file with the findings and start drafting a technical paper to be shared with Amazon AI forume. Mid-morning, you're in a design session with the engineering team discussing how to optimize RAG-based embeddings for semantic search over advertiser data. You propose using a hybrid approach combining dense and sparse embeddings to represent campaign metadata, enabling agents to find relevant campaigns through natural language queries while maintaining sub-second latency. You sketch out the architecture and discuss trade-offs between embedding model size, search latency, and accuracy. After lunch, you dive into advertiser interaction logs from advertising agents and skills. You're looking for patterns in how advertisers ask questions about their campaigns. You discover that 60% of queries follow a similar structure: filter campaigns by criteria, aggregate metrics, and compare to benchmarks. This insight leads you to design a new pre-computation strategy using RAG-based embeddings that could reduce query latency by 40%. In the afternoon, you collaborate with an Applied Scientist from an advertising agent team. They're seeing inconsistent results when agents try to calculate complex metrics across multiple campaigns. You investigate and discover the issue is related to how the agent interprets the advertiser context. You propose enriching the RAG-based embeddings with richer metadata descriptions and run experiments showing this improves calculation accuracy from 85% to 98%. Late afternoon, you're prototyping a new approach for adaptive context selection using RAG-based embeddings with the spec file you generated earlier. Instead of providing agents with all available advertiser data, you want to dynamically select the most relevant datasets based on query intent using semantic similarity. You build a quick proof-of-concept and test it on historical queries. The results are promising: 30% reduction in tokens with no loss in response quality. About the team The Ads Real-Time Data Service team is a highly motivated, collaborative and fun-loving group of engineers building the foundational platform for Amazon's advertising AI future. We are entrepreneurial and have a bias for action with a broad mandate to experiment and innovate. Our team operates at the intersection of real-time data engineering, AI agent infrastructure, and distributed systems engineering—solving problems that directly impact how millions of advertisers interact with Amazon's advertising products. We value technical excellence, customer obsession, and sustainable engineering practices. Our team includes engineers with diverse backgrounds in distributed systems, real-time data processing, AI/ML infrastructure, and platform engineering. We celebrate innovation (patent submissions encouraged), knowledge sharing (weekly tech talks), and continuous learning. We maintain a sustainable pace with minimal on-call burden, flexible work arrangements, and a strong focus on work-life balance. We're at the forefront of AI-assisted development, using tools like Kiro to accelerate our development cycles from weeks to days.

Are you passionate about building the world's largest low-power, long-range IoT network? Do you want to use data science to expand connectivity for billions of devices and directly shape how Amazon Sidewalk grows its coverage footprint? If turning IoT telemetry into actionable insights excites you, we want to talk. The Amazon Sidewalk team is looking for an analytical and technically skilled Data Scientist to join our team. In this role, you will design, build, and deploy machine learning models and analytical solutions that power Sidewalk's coverage map, optimize network performance, and drive insights from one of the largest mesh IoT networks in the world. Amazon Sidewalk is a shared network that helps devices like Ring, Echo, and third-party IoT endpoints work better at home and beyond the front door. Sidewalk uses Bluetooth Low Energy (BLE), 900 MHz LoRa, and FSK radio protocols to extend the working range of low-bandwidth devices through a network of millions of Sidewalk-enabled gateways. This role puts you at the center of understanding and expanding that network. This role offers wide scope, autonomy, and ownership. You will work closely with software engineers, data engineers, and product managers to put algorithms into practice. You should have strong business judgment, excellent written and verbal communication skills, and be willing to take on challenging initiatives both independently and as part of a team. Key job responsibilities - Build and improve Sidewalk's coverage forecast models that predict network availability across BLE, LoRa, and FSK link types for any geographic location. - Analyze gateway telemetry, device event data, and registration statistics from the Sidewalk Data Lakehouse to identify coverage gaps, network health trends, and device behavior patterns. - Develop geospatial models that leverage gateway location data, signal propagation characteristics, and device density to optimize coverage map accuracy. - Design anomaly detection systems to identify gateway outages, data limit issues, key refresh failures, and device provisioning problems at scale. - Research and develop Generative AI and LLM-based solutions for automated device troubleshooting and root cause analysis across the Sidewalk network. - Define and track key network health metrics including coverage scores, gateway online rates, device registration success rates, and data usage patterns. - Drive end-to-end statistical analysis on complex, ambiguous problems such as predicting optimal gateway placement, modeling RF propagation in urban vs. suburban environments, and forecasting network growth. - Partner with engineering teams to productionize models into Sidewalk's cloud services, ensuring coverage predictions and device analytics are available in real time. About the team The Amazon Sidewalk team builds and operates a crowd-sourced IoT network, connecting millions of gateways (Ring, Echo, and third-party devices) to provide low-bandwidth connectivity for IoT endpoints. Our platform handles device provisioning, authentication, data routing across BLE/LoRa/FSK protocols, coverage forecasting, and gateway management. The network supports use cases ranging from smart home device connectivity to asset tracking and pet location services. Our data infrastructure processes hundreds of millions of device events daily serving insights to engineering, product, and partner teams.

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

Come join the AWS Agentic AI science team in building the next generation models for intelligent automation. AWS, the world-leading provider of cloud services, has fostered the creation and growth of countless new businesses, and is a positive force for good. Our customers bring problems that will give Applied Scientists like you endless opportunities to see your research have a positive and immediate impact in the world. You will have the opportunity to partner with technology and business teams to solve real-world problems, have access to virtually endless data and computational resources, and to world-class engineers and developers that can help bring your ideas into the world. As part of the team, we expect that you will develop innovative solutions to hard problems, and publish your findings at peer reviewed conferences and workshops. We are looking for world class researchers with experience in one or more of the following areas - autonomous agents, API orchestration, Planning, large multimodal models (especially vision-language models), reinforcement learning (RL) and sequential decision making.

Are you interested in shaping the future of Advertising and B2B Sales? We are a growing science and engineering team with an exciting charter and need your passion, innovative thinking, and creativity to help take our products to new heights. Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising products that drive discovery and sales. Our products are strategically important to our businesses driving long term growth. We break fresh ground in product and technical innovations every day! Within the Advertising Sales organization, we are building a central AI/ML team and are seeking top science talent to build new, science-backed services to drive success for our customers. Our goal is to transform the way account teams operate by creating actionable insights and recommendations they can share with their advertising accounts, and ingesting Generative AI throughout their end-to-end workflows to improve their work efficiency. As a part of our team, you will bring deep expertise in Generative AI and quantitative modeling (forecasting, recommender systems, reinforcement learning, causal inferencing or generative artificial intelligence) to build and refine models that can be implemented in production. You will contribute to chart new courses with our ad sales support technologies, and you have the communication skills necessary to explain complex technical approaches to a variety of stakeholders and customers. You will be part of a team of fellow scientists and engineers taking on iterative approaches to tackle big, long-term problems. Why you will love this opportunity: Amazon has invested heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon's Retail and Marketplace businesses. We deliver billions of ads impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit with a broad mandate to experiment and innovate. Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences; this is your opportunity to work within the fastest growing businesses across all of Amazon! Define a long-term scientific vision for our advertising sales business, driven from our customers' needs, translating that direction into specific plans for scientists, engineers and product teams. This role combines scientific leadership, organizational ability, technical strength, product focus, and business understanding. Key job responsibilities - Conceptualize and lead state-of-the-art research on new Machine Learning and Generative Artificial Intelligence solutions to optimize all aspects of the Ad Sales business - Guide the technical approach for the design and implementation of successful models and algorithms in support of expert cross-functional teams delivering on demanding projects - Conduct deep data analysis to derive insights to the business, and identify gaps and new opportunities - 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 About the team Sales AI is a central science and engineering organization within Amazon Advertising Sales that powers selling motions and account team workflows via state-of-the-art of AI/ML services. Sales AI is investing in a range of sales intelligence models, including the development of advertiser insights, recommendations and Generative AI-powered applications throughout account team workflows.