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MULTIPLE POSITIONS AVAILABLE Employer: AMAZON.COM SERVICES LLC Offered Position: Data Scientist III Job Location: Austin, Texas Job Number: AMZ9442227 Position Responsibilities: Own the data science elements of various products to help with data-based decision making, product performance optimization, and product performance tracking. Work directly with product managers to help drive the design of the product. Work with Technical Product Managers to help drive the build planning. Translate business problems and products into data requirements and metrics. Initiate the design, development, and implementation of scientific analysis projects or deliverables. Own the analysis, modelling, system design, and development of data science solutions for products. Write documents and make presentations that explain model/analysis results to the business. Bridge the degree of uncertainty in both problem definition and data scientific solution approaches. Build consensus on data, metrics, and analysis to drive business and system strategy. Position Requirements: Master's degree or foreign equivalent degree in Statistics, Applied Mathematics, Economics, Engineering, Computer Science or a related field and two years of experience in the job offered or a related occupation. Employer will accept a Bachelor's degree or foreign equivalent degree in Statistics, Applied Mathematics, Economics, Engineering, Computer Science, or a related field and five years of progressive post-baccalaureate experience in the job offered or a related occupation as equivalent to the Master's degree and two years of experience. Must have one year of experience in the following skills: (1) building statistical models and machine learning models using large datasets from multiple resources; (2) building complex data analyses by leveraging scripting languages including Python, Java, or related scripting language; and (3) communicating with users, technical teams, and management to collect requirements, evaluate alternatives, and develop processes and tools to support the organization. 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 $159,200/year to $215,300/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

We are seeking a talented, customer-focused Senior Applied Scientist to join the AVS ProServe Science and Data Team. In this role, you will develop and apply machine learning algorithms to solve ambiguous business problems, leading the roadmap to help vendors understand their customers, identify opportunities for growth and define long-term strategy. The ideal candidate brings deep expertise in machine learning, interpretable models, transformers, and experimentation, along with the business acumen to translate problems into scalable science solutions. We're looking for a self-starter with an entrepreneurial spirit who is comfortable with ambiguity, demonstrates strong attention to detail, and thrives in a fast-paced, data-driven environment — with a passion for driving measurable impact. Key job responsibilities • Develop customer understanding models by designing and building orchestrated ML solutions that enable vendors to deeply understand their customers and uncover growth opportunities • Bridge science and business strategy by translating model outputs into actionable insights and recommendations that inform vendor growth strategies, customer acquisition, and long-term planning • Measure and validate business impact by closing the loop between science solutions and business outcomes — establishing measurement frameworks that quantify impact, surface new opportunities, and continuously refine the path to vendor growth • Lead cross-functional collaboration by working with engineers, scientists, consultants, and business leaders to deploy scalable solutions while communicating complex technical concepts clearly to non-technical audiences • Stay at the forefront of innovation by applying state-of-the-art techniques in machine learning, interpretable models, and transformers to solve ambiguous business problems while fostering rapid experimentation and continuous learning

The Alexa AI, AURORA: Alexa Understanding, Runtime, ORchestration, and Applied sciences org is seeking a passionate, talented, and resourceful Senior Applied Scientist to invent and build scalable solutions for sate-of-the-art conversational AI. You will be working on advancing technologies in the fields of LLM, Artificial Intelligence (AI), Natural Language Processing (NLP). As part of this role, you will collaborate with talented peers, have significant influence on our overall strategy as you guide and mentor Applied Scientists to create innovative and scalable solutions that touch millions of Alexa customers. Creating reliable, scalable, and high performance products requires exceptional technical expertise, a sound understanding of the fundamentals of AI, and practical experience building large-scale machine learning systems. The ideal candidate will be a self-starter who can dive into a project with limited guidance and is able to design and implement inventive, simple solutions to complex problems. They will be passionate about new technologies and have a track record of delivering valuable software features and products in a fast-paced, highly iterative environment. A commitment to team work, hustle, and strong communication skills (to both business and technical partners) are absolute requirements. Join us in our mission to shape the space of Generative AI and provide unparalleled experiences for our customers. Key job responsibilities • Define the science roadmap and advance core science primitives for conversation modelling, content generation, and automated quality assurance via state-of-the-art computer vision, machine learning, and generative AI • Architect agentic systems, making high-judgment trade-offs across audio/text/visual quality, relevance, latency, cost, and long-term extensibility • Establish evaluation frameworks, metrics, and success criteria for the team's scientific initiatives, institutionalizing rigorous validation across customer touch points • Own end-to-end delivery of complex, ambiguous research initiatives from problem formulation through experimentation to production deployment, with minimal guidance • Identify whitespace opportunities by staying at the forefront of AI/ML advances and translating them into actionable research directions with clear customer and business impact • Drive development and deployment of scalable agentic systems for conversation understanding and generation, ensuring architectural decisions support long-term platform evolution • Set and continuously raise the scientific and engineering bar across the team • Tackle the team's most complex technical problems while maintaining practical focus on customer value and solution generalizability • Advance the team's scientific reputation through high-impact publications and presentations at top-tier internal and external venues, and generate intellectual property through patents About the team AURORA is the AI runtime backbone and horizontal intelligence team that powers Alexa's core infrastructure, AI capabilities, and specialized conversational models. We revolutionize conversational AI through three core pillars: architecting mission-critical AI runtime systems, advancing science solutions that connect key conversational capabilities, and transforming how builders create at scale. We empower 1P and 3P engineers and scientists worldwide with modular, reusable platforms that accelerate innovation while delivering accurate, responsive, and reliable conversational experiences to millions of end-users through operational excellence at scale.

Amazon Leo is an initiative to increase global broadband access through a constellation of 3,236 satellites in low Earth orbit (LEO). Its mission is to bring fast, affordable broadband to unserved and underserved communities around the world. Amazon Leo will help close the digital divide by delivering fast, affordable broadband to a wide range of customers, including consumers, businesses, government agencies, and other organizations operating in places without reliable connectivity. Do you get excited by aerospace, space exploration, and/or satellites? Do you want to help build solutions at Amazon Leo to transform the space industry? If so, then we would love to talk! Key job responsibilities Work cross-functionally with product, business development, and various technical teams (engineering, science, simulations, etc.) to execute on the long-term vision, strategy, and architecture for the science-based global demand forecast. Design and deliver modern, flexible, scalable solutions to integrate data from a variety of sources and systems (both internal and external) and develop Bandwidth Usage models at granular temporal and geographic grains, deployable to Leo traffic management systems. Work closely with the capacity planning science team to ensure that demand forecasts feed seamlessly into their systems to deliver continuous optimization of resources. Lead short and long terms technical roadmap definition efforts to deliver solutions that meet business needs in pre-launch, early-launch, and mature business phases. Synthesize and communicate insights and recommendations to audiences of varying levels of technical sophistication to drive change across Amazon Leo. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum. About the team The Amazon Leo Global Demand Planning team's mission is to map customer demand across space and time. We enable Amazon Leo's long-term success by delivering actionable insights and scientific forecasts across geographies and customer segments to empower long range planning, capacity simulations, business strategy, and hardware manufacturing recommendations through scalable tools and durable mechanisms.

The Stores Economics and Science (SEAS) team uses Economics, Statistics, and Machine Learning to understand and design the complex economy of Amazon’s network of buyers and sellers. We are an interdisciplinary team, committed to use of cutting edge technology and leveraging the strengths of engineers and scientists to build solutions for some of the toughest business problems at Amazon. We are looking for an outstanding science manager who is able to provide structure around complex business problems, work with machine learning scientists to estimate and validate their models on large scale data, and who can help business and tech partners turn the results of their analysis into policies, programs, and actions that have a major impact on Amazon’s business. We are looking for creative thinkers who can combine a strong economic toolbox with a desire to learn from others, and who know how to execute and deliver on big ideas. Experience in applied economic analysis is essential, and you should be familiar with modern tools for data science and business analysis.

Are you excited by the idea of developing personalized experiences for Amazon customers as they shop? Are you looking for new challenges and to solve hard science problems while applying state-of-the-art recommendation system modeling and GenAI techniques? Join us and you'll help millions of customers make informed purchase decisions while also advancing the state of Amazon's science by publishing research! Key job responsibilities - Participate in the design, development, evaluation, deployment and updating of data-driven models for shopping personalization. - Develop and test new signals for improving recommendation models - Integrate GenAI into Amazon customer shopping experience - Design A/B tests and conduct statistical analysis on their results - Work with distributed machine learning and statistical algorithms to harness enormous volumes of data at scale to 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 and publish science research, contributing to Amazon's science community - Mentor junior engineers and scientists. 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.

Amazon Leo is Amazon’s low Earth orbit satellite network. Our mission is to deliver fast, reliable internet connectivity to customers beyond the reach of existing networks. From individual households to schools, hospitals, businesses, and government agencies, Amazon Leo will serve people and organizations operating in locations without reliable connectivity. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum. This position is part of the Satellite Attitude Determination and Control team. You will design and analyze the control system and algorithms, support development of our flight hardware and software, help integrate the satellite in our labs, participate in flight operations, and see a constellation of satellites flow through the production line into orbit. Key job responsibilities - Design and analyze algorithms for estimation, flight control, and precise pointing using linear methods and simulation. - Develop and apply models and simulations, with various levels of fidelity, of the satellite and our constellation. - Component level environmental testing, functional and performance checkout, subsystem integration, satellite integration, and in space operations. - Manage the spacecraft constellation as it grows and evolves. - Continuously improve our ability to serve customers by maximizing payload operations time. - Develop autonomy for Fault Detection and Isolation on board the spacecraft. A day in the life This is an opportunity to play a significant role in the design of an entirely new satellite system with challenging performance requirements. The large, integrated constellation brings opportunities for advanced capabilities that need investigation and development. The constellation size also puts emphasis on engineering excellence so our tools and methods, from conceptualization through manufacturing and all phases of test, will be state of the art as will the satellite and supporting infrastructure on the ground. You will find that Amazon Leo's mission is compelling, so our program is staffed with some of the top engineers in the industry. Our daily collaboration with other teams on the program brings constant opportunity for discovery, learning, and growth. About the team Our team has lots of experience with various satellite systems and many other flight vehicles. We have bench strength in both our mission and core GNC disciplines. We design, prototype, test, iterate and learn together. Because GNC is central to safe flight, we tend to drive Concepts of Operation and many system level analyses.

Are you excited by the idea of developing personalized experiences for Amazon customers as they shop? Are you looking for new challenges and to solve hard science problems while applying state-of-the-art recommendation system modeling and GenAI techniques? Join us and you'll help millions of customers make informed purchase decisions while also advancing the state of Amazon's science by publishing research! Key job responsibilities - Participate in the design, development, evaluation, deployment and updating of data-driven models for shopping personalization. - Develop and test new signals for improving recommendation models - Use supervised and uplift learning algorithms to improve customer experience - Contribute to production code and science tooling - Design A/B tests and conduct statistical analysis on their results - Work with distributed machine learning and statistical algorithms to harness enormous volumes of data at scale to 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 and publish science research internally and externally, contributing to Amazon's science community - Mentor junior engineers and scientists. 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.

As a Data Scientist in Alexa Connections, you will lead the end-to-end development of machine learning and data science solutions that power intelligent communication experiences across channels such as calling, messaging and email. You will partner closely with product, engineering, and business leaders to translate ambiguous problems into scalable ML models, experimentation frameworks, and data-driven product decisions. In this role, you will design and deploy advanced ML and statistical models for capabilities such as prioritization, intent detection, and proactive action recommendations. You will analyze large-scale datasets and run rigorous experiments, including A/B testing and causal analysis, to measure impact and continuously improve customer engagement and product performance. Additionally, you will shape the applied science roadmap and collaborate with global cross-functional teams to deliver AI-driven solutions that scale to millions of Alexa customers. Key job responsibilities - Partner with product, engineering, operations, and security teams to translate complex business problems into scalable, production-ready data science and ML solutions. - Own the full lifecycle of model development, including exploratory analysis, model design, deployment, monitoring, and continuous improvement. - Define and track success metrics, conduct rigorous analyses, and provide insights that guide product launches, feature improvements, and data-driven decision-making. - Build data-driven business cases to prioritize science initiatives and demonstrate measurable impact of ML solutions. - Develop ML-powered systems supporting key business areas. - Lead research and analysis to understand customer interactions with Alexa, and enhance overall customer experience. - Contribute to the broader science community by mentoring analysts, improving data workflows and tooling, and publishing technical work in internal and external forums. A day in the life • Deep dive into our business metrics, analyze data, trends, and reviewing dashboards • Writing code: building packages in Python, writing SQL queries, deploying solutions for Connections experience teams to consume. • Leading or joining working sessions with Product Managers to refine problem statements new initiatives. • Exploring new features and model architectures, leveraging AWS services, documentation, and upskilling yourself to the latest technologies. • Leverage pre-trained LLMs to build applications that solve business problems for Connections experiences. • Meet with Sr. Engineers/Principal Engineers to align on solution designs. • Own or co-own MBR, WBR documents that are reviewed with Connections leadership team.

Alexa Connections is looking for a passionate, talented, and inventive Applied Scientist to help build industry-leading technology with Large Language Models (LLMs) and multimodal systems, requiring strong deep learning and generative models knowledge. You will contribute to developing novel solutions and deliver high-quality results that impact Connections products and services. Key job responsibilities As an Applied Scientist with the Alexa Connections team, you will work with talented peers to develop novel algorithms and modeling techniques to advance the state of the art with LLMs. Your work will directly impact our customers in the form of products and services that make use of digital assistant technology. You will leverage Amazon's heterogeneous data sources, unique and diverse international customer nuances and large-scale computing resources to accelerate advances in text, voice, and vision domains in a multimodal setup. The ideal candidate possesses a solid understanding of machine learning, natural language understanding, modern LLM architectures, LLM evaluation & tooling, and a passion for pushing boundaries in this vast and quickly evolving field. They thrive in fast-paced environments to tackle complex challenges, excel at swiftly delivering impactful solutions while iterating based on user feedback, and collaborate effectively with cross-functional teams. A day in the life * Analyze, understand, and model customer behavior and the customer experience based on large-scale data. * Build novel online & offline evaluation metrics and methodologies for multimodal personal digital assistants. * Fine-tune/post-train LLMs using techniques like SFT, DPO, RLHF, and RLAIF. * Set up experimentation frameworks for agile model analysis and A/B testing. * Collaborate with partner teams on LLM evaluation frameworks and post-training methodologies. * Contribute to end-to-end delivery of solutions from research to production, including reusable science components. * Communicate solutions clearly to partners and stakeholders. * Contribute to the scientific community through publications and community engagement.