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MULTIPLE POSITIONS AVAILABLE Employer: AMAZON DATA SERVICES, INC. Offered Position: Data Scientist III Job Location: Seattle, Washington Job Number: AMZ9706811 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 $165,006/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

Do you want to join a team of innovative scientists to research and develop generative AI technology that would disrupt the industry? Do you enjoy dealing with ambiguity and working on hard problems in a fast-paced environment? Amazon Connect is a highly disruptive cloud-based contact center from AWS that enables businesses to deliver intelligent, engaging, dynamic, and personalized customer service experiences. The Agentic Customer Experience (ACX) org is responsible for weaving native-AI across the Connect application experiences delivered to end-customers, agents, and managers/supervisors. The Interactive AI Science team, serves as the cornerstone for AI innovation across Amazon Connect, functioning as the sole science team support high impact product including Amazon Q in Connect, Contact Lens and other key initiatives. As an Applied Scientist on our team, you will work closely with senior technical and business leaders from within the team and across AWS. You distill insight from huge data sets, conduct cutting edge research, foster ML models from conception to deployment. You have deep expertise in machine learning and deep learning broadly, and extensive domain knowledge in natural language processing, generative AI and LLM Agents evaluation and optimization, etc. You are comfortable with quickly prototyping and iterating your ideas to build robust ML models using technology such as PyTorch, Tensorflow and AWS Sagemaker. The ideal candidate has the ability to understand, implement, innovate on the state-of-the-art Agentic AI based systems. We have a rapidly growing customer base and an exciting charter in front of us that includes solving highly complex engineering and scientific problems. We are looking for passionate, talented, and experienced people to join us to innovate on modern contact centers in the cloud. The position represents a rare opportunity to be a part of a fast-growing business soon after launch, and help shape the technology and product as we grow. You will be playing a crucial role in developing the next generation contact center, and get the opportunity to design and deliver scalable, resilient systems while maintaining a constant customer focus. About the team Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the 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 Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. 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.

Amazon is seeking a Language Data Scientist to join the Alexa Artificial Intelligence (AI) team as domain expert. This role focuses on expanding analysis and evaluation of speech and interaction data deliverables. The Language Data Scientist is an expert in dialog evaluation processes, working closely with a team of skilled analysts and machine learning scientists and engineers, and is a key member in developing new conventions for relevant annotation workflows. The Language Data Scientist will be asked to handle unique data analysis and research requests that support the training and evaluation of machine learning models and the overall processing of a data collection. Key job responsibilities To be successful in this role, you must have a passion for data, efficiency, and accuracy. Specifically, you will: - Own data analyses for customer-facing features, including launch go/no-go metrics for new features and accuracy metrics for existing features - Handle unique data analysis requests from a range of stakeholders, including quantitative and qualitative analyses to elevate customer experience with speech interfaces - Lead and evaluate changing dialog evaluation conventions, test tooling developments, and pilot processes to support expansion to new data areas - Continuously evaluate workflow tools and processes and offer solutions to ensure they are efficient, high quality, and scalable - Provide expert support for a large and growing team of data analysts - Provide support for ongoing and new data collection efforts as a subject matter expert on conventions and use of the data - Conduct research studies to understand speech and customer-Alexa interactions - Assist scientists, program and product managers, and other stakeholders in defining and validating customer experience metrics

Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. We offer experiences that serve all listeners with our different tiers of service: Prime members get access to all the music in shuffle mode, and top ad-free podcasts, included with their membership; customers can upgrade to Amazon Music Unlimited for unlimited, on-demand access to 100 million songs, including millions in HD, Ultra HD, and spatial audio; and anyone can listen for free by downloading the Amazon Music app or via Alexa-enabled devices. Join us for the opportunity to influence how Amazon Music engages fans, artists, and creators on a global scale. The Data, Insights, Science and Optimization, Consumer Product and Tech (DISCO CPT) team is looking for an Data Science Lead to join a team of Data Scientists, Analytics Leads, Business Intelligence Engineers and Data Engineers who analyze big data, provide analytics and insights as well as build models and algorithms that power product for personalized experiences. DISCO MPT team focuses on accelerating Amazon Music customer growth by empowering Product teams to make sound, customer-centric decisions through data and insights. We build data pipelines, science models, self-service analytics, insights and dashboards, deliver engagement and retention at scale. In this role, you will lead the analytics and insights efforts for one of the Amazon Music product teams. You will set the science and analytics roadmap and direction for the domain and work with stakeholders and partners to deliver experimentation insights, authoring white papers and models to advance our analytics and science offerings. The ideal candidate must be willing to effectively lead science solutions, prioritize across multiple stakeholders and projects and be ready to jump into a fast-paced, dynamic and fun environment. Key job responsibilities * A Data Science Lead serving this role will act as thought partner to L8s of Product and Engineering organizations * Lead and drive Data Scientists on strategic growth areas: retention, voice + visual engagement, organization-wide experimentation, Gen AI initiatives, and product excellence. * Bridge science, analytics, and engineering to deliver solutions informing strategic decisions on AM technology investments and long-term business performance * Drive experimentation measurement efficiency tooling, and ROI tracking for AM leadership * Build and maintain customer segmentation models to understand streaming behaviors and its impact on key engagement/retention metrics * Develop structural and predictive models using data science workflows to deliver measurable customer results A day in the life * Lead and mentor data scientists specializing in experimentation and customer analytics * Lead strategic relationships across music search, experimentation, music catalog and FPE initiatives * Conduct experimentation and hypothesis testing, build launch measurements, goal benchmarking, and forecasting * Develop science models for targeting and create operational reporting with anomaly detection for product health monitoring * Partner with product managers to translate business questions into actionable science projects * Influence product roadmaps through data-driven insights on customer behavior patterns * Conduct stakeholder meetings and present insights to senior leadership * Identify and resolve customer experience friction points with cross-functional teams * Drive adoption of best practices and innovative solutions across the organization

Amazon.com’s Buyer Risk Prevention (BRP) mission is to make Amazon the safest and most trusted place worldwide to transact online. Amazon runs one of the most dynamic e-commerce marketplaces in the world, with nearly 2 million sellers worldwide selling hundreds of millions of items in ten countries. BRP safeguards every financial transaction across all Amazon sites. As such, BRP designs and builds the software systems, risk models, and operational processes that minimize risk and maximize trust in Amazon.com. The BRP organization is looking for an Applied Scientist for its Payment Risk Mining ML team, whose mission is to combine advanced machine learning techniques to detect negative customer experiences, improve system effectiveness, and prevent bad debt across Amazon. As an Applied Scientist in Risk Mining, you will be responsible for modeling complex problems, discovering insights, and building risk algorithms that identify opportunities through state-of-the-art techniques including statistical models, deep learning, large language models (LLMs), and agentic AI systems. You will explore and implement state of the art approaches such as graph neural networks, anomaly detection, GenAI-powered investigation agents, and multi-agent systems to automate fraud detection and prevention at scale. You will build and deploy production-ready models and automated systems to improve operational efficiency and reduce bad debt. You will collaborate effectively with business and product leaders within BRP and cross-functional teams to build scalable solutions. The ideal candidate should be able to apply a breadth of tools, and advanced ML techniques, from traditional machine learning to emerging agentic frameworks, to take ideas from experimentation to production, driving tangible improvements in fraud detection and prevention. The candidate should be an effective communicator capable of independently driving issues to resolution and communicating insights to non-technical audiences. This is a high-impact role with goals that directly impact the bottom line of the business. Key job responsibilities Own end-to-end development of machine learning models for large-scale risk management systems Analyze large volumes of historical and real-time data to identify fraud patterns and emerging risk trends Design, develop, validate, and deploy innovative models to production environments Apply GenAI/LLM technologies to automate risk evaluation and improve operational efficiency Collaborate closely with software engineering teams to implement scalable, real-time model solutions Partner with operations and business stakeholders to translate risk insights into measurable impact Establish scalable and automated processes for data analysis, model experimentation, validation, and monitoring Track model performance and business metrics; communicate insights clearly to technical and non-technical stakeholders Research and implement novel machine learning and statistical methodologies

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 - 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 evaluations 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 30+ advertising agents and skills - Establish evaluation metrics and benchmarks for agent-data interaction performance Cross-Functional Collaboration & Thought Leadership - Partner with agent builder teams to understand their data requirements and constraints - Work with platform engineers to implement and optimize MCP servers, data pipelines, and sandbox execution environments - Collaborate with product managers to translate research insights into product features and roadmap priorities - Stay current on latest advancements in agentic AI research, specifically in large language models, multi-agent systems, chain of thought reasoning, and autonomous agents Research Publication & Innovation - Author technical papers for top-tier conferences on agent orchestration, context optimization, RAG-based embeddings, and real-time data integration - File patents for novel techniques in agent-data interaction, token optimization, and CodeAct patterns - Present research findings at internal tech talks and external conferences - Mentor engineers and junior scientists on machine learning techniques, experimental design, and research methodologies 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 forum. 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 diverse group of passionate engineers and scientists dedicated to advancing agent-data interaction technology for advertising AI. We value creativity, collaboration, and a commitment to excellence. Our team thrives on tackling complex problems at the intersection of real-time data engineering, AI agent systems, and large language model optimization—turning innovative research ideas into production systems that serve millions of advertisers. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and bias for action. We have a broad mandate to experiment and innovate, working on problems in agentic AI, context optimization, RAG-based embeddings, and real-time data delivery. We celebrate both research excellence (papers, patents) and engineering impact (production systems serving 30+ advertising agents and skills). We maintain a sustainable pace with flexible work arrangements and a strong focus on work-life balance.

The Mission Build AI safety systems that protect millions of Alexa customers every day. As conversational AI evolves, you'll solve challenging problems in Responsible AI by ensuring LLMs provide safe, trustworthy responses, building AI systems that understand nuanced human values across cultures, and maintaining customer trust at scale. What You'll Build You'll pioneer breakthrough solutions in Responsible AI at Amazon's scale. Imagine training models that set new safety standards, designing automated testing systems that hunt for vulnerabilities before they surface, and certifying the systems that power millions of daily conversations. You'll create intelligent evaluation systems that judge responses with human-level insight, build models that truly understand what makes interactions safe and delightful, and craft feedback mechanisms that help Alexa+ grasp the nuances of complex customer conversations. Here's where it gets even more exciting: you'll build AI agents that act as your team's safety net—automatically detecting and fixing production issues in real-time, often before anyone notices there was a problem. Your innovations won't just improve Alexa+; they'll fundamentally shape how it learns, evolves, and earns customer trust. As Alexa+ continues to delight customers, your work ensures it becomes more trustworthy, safer, and deeply aligned with customer needs and expectations. Your work directly protects customer trust at Amazon's scale. Every innovation you create—from novel safety mechanisms to sophisticated evaluation techniques—shapes how millions of people interact with AI confidently. You're not just building products; you're defining industry standards for responsible AI. This is frontier research with immediate real-world impact. You'll tackle problems that require innovative solutions: training models that remain truthful and grounded across diverse contexts, building reward models that capture the nuanced spectrum of human values across cultures and languages, and creating automated systems that continuously discover and address potential issues before customers encounter them. You'll collaborate with world-class scientists, product managers, and engineers to transform state-of-the-art ideas into production systems serving millions. What We're Looking For * Deep expertise in state-of-the-art NLP and Large Language Models * Track record of building scalable ML systems * Passion for impactful research—where frontier science meets real-world responsibility at scale * Excitement about solving problems that will shape the future of AI Ready to work on AI safety challenges that define the industry? Join us. Key job responsibilities This is where you'll make your mark. You'll architect breakthrough Responsible AI solutions that become industry benchmarks, pioneering algorithms that eliminate false information, designing frameworks that hunt down vulnerabilities before bad actors find them, and developing models that understand human values across every culture we serve. Working with world-class engineers and scientists, you'll push the boundaries of model training—transforming bold research into production systems that protect millions of customers daily while withstanding attacks and delivering exceptional experiences. But here's what makes this role truly special: you'll shape the future. You'll lead certification processes, advance optimization techniques, build evaluation systems that reason like humans, and mentor the next generation of AI safety experts. Every innovation you drive will set new standards for trustworthy AI at the world's largest scale. A day in the life As a Responsible AI Scientist, you're at the frontier of AI safety—experimenting with breakthrough techniques that push the boundaries of what's possible. You partner with engineering to transform research into production-ready solutions, tackling complex optimization challenges. You brainstorm with Product teams, translating ambitious visions into concrete objectives that drive real impact. Your expertise shapes critical deployment decisions as you review impactful work and guide go/no-go calls. You mentor the next generation of AI safety leaders, watching ideas spark and capabilities grow. This is where science meets impact—building AI that's not just intelligent, but trustworthy and aligned with human values. About the team Our team pioneers Responsible AI for conversational assistants. We ensure Alexa delivers safe, trustworthy experiences across all devices, modalities, and languages worldwide. We work on frontier AI safety challenges—and we're looking for scientists who want to help shape the future of trustworthy AI.

Stores Economics and Science (SEAS) is an interdisciplinary science and engineering team in Amazon's Stores organization with a peak-jumping mission: we apply expertise in science and engineering to move from local to global optima in methods, models, and software. We pursue this mission by leveraging frontier science; collaborating with partner teams; and learning from the tools, experience, and perspective of others. We scale by solving problems, first in the small to prove concepts, and then in the large by building scalable solutions. We also help other teams within Amazon scale by hiring and developing the best and embedding them in other business units. We are team of passionate Applied Scientists, Research Scientists, and Economists collaborating to uncover, formulate, and solve big bet issues for broader Stores organization, including marketplace efficiency, seller incentive design, and inventory optimization. We are seeking to add another member to our team who thrives in inter-disciplinary, ambiguous, and high visibility environments. Key job responsibilities The successful candidate will lead large-scale science initiatives from research to production and translate complex business problems into mathematical frameworks. They will design and implement large-scale optimization algorithms for complex supply chain and marketplace problems, and design incentive-compatible mechanisms for marketplace challenges including auction design, matching algorithms, and pricing strategies. The ideal candidate will have a strong publication record in top-tier conferences/journals (INFORMS, EC, WINE, ICML, NeurIPS, etc.) and experience coordinating cross-functional projects. They should have hands-on experience building science solutions that apply optimization techniques to mechanism design problems (e.g., optimal auction design, welfare maximization under constraints), with expertise in statistical learning and algorithm development. Leadership responsibilities include driving technical strategy and roadmap for complex initiatives, influencing senior stakeholders and shaping technical direction, mentoring 2-4 junior scientists and fostering team growth, and collaborating effectively across functions to deliver measurable business impact.

The Amazon Center for Quantum Computing (CQC) is a multi-disciplinary team of scientists, engineers, and technicians, on a mission to develop a fault-tolerant quantum computer. We are looking to hire an Instrument Control Engineer to join our growing software team. You will work closely with our experimental physics and control hardware development teams to enable their work characterizing, calibrating, and operating novel quantum devices. The ideal candidate should be able to translate high-level science requirements into software implementations (e.g. Python APIs/frameworks, compiler passes, embedded SW, instrument drivers) that are performant, scalable, and intuitive. This requires someone who (1) has a strong desire to work within a team of scientists and engineers, and (2) demonstrates ownership in initiating and driving projects to completion. This role has a particular emphasis on working directly with our control hardware designers and vendors to develop instrument software for test and measurement. Inclusive Team Culture Here at Amazon, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon conferences, inspire us to never stop embracing our uniqueness. Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the 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. 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. Export Control Requirement Due to applicable export control laws and regulations, candidates must be either 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, or be able to obtain a US export license. If you are unsure if you meet these requirements, please apply and Amazon will review your application for eligibility. Key job responsibilities - Work with control hardware developers, as a “subject matter expert” on the software interfaces around our control hardware - Collaborate with external control hardware vendors to understand and refine integration strategies - Implement instrument drivers and control logic in Python and/or a low-level languages, including C++ or Rust - Contribute to our compiler backend to enable the efficient execution of OpenQASM-based experiments on our next-generation control hardware - Benchmark system performance and help define key performance metrics - Ensure new features are successfully integrated into our Python-based experimental software stack - Partner with scientists to actively contribute to the codebase through mentorship and documentation We are looking for candidates with strong engineering principles, a bias for action, superior problem-solving, and excellent communication skills. Working effectively within a team environment is essential. As an Instrument Control Engineer embedded in a broader science organization, you will have the opportunity to work on new ideas and stay abreast of the field of experimental quantum computation. A day in the life Your time will be spent on projects that extend functional capabilities or performance of our internal research software stack. This requires working backwards from the needs of science staff in the context of our larger experimental roadmap. You will translate science and software requirements into design proposals balancing implementation complexity against time-to-delivery. Once a design proposal has been reviewed and accepted, you’ll drive implementation and coordinate with internal stakeholders to ensure a smooth roll out. Because many high-level experimental goals have cross-cutting requirements, you’ll often work closely with other engineers or scientists or on the team. About the team You will be joining the Software group within the Amazon Center of Quantum Computing. Our team is comprised of scientists and software engineers who are building scalable software that enables quantum computing technologies.

Are you interested in building the measurement foundation that proves whether targeted, cohort-based marketing actually changes customer behavior at Amazon scale? We are seeking an Applied Scientist to own measurement and experimentation for our Lifecycle Marketing Experimentation roadmap within the PRIMAS (Prime & Marketing Analytics and Science) team. In this role, you will design and execute rigorous experiments that measure the effectiveness of audience-based marketing campaigns across multiple channels, providing the evidence that guides marketing strategy and investment decisions. This is a high-impact role where you will build measurement frameworks from scratch, design experiments that isolate causal effects, and establish the experimental standards for lifecycle marketing across EU. You will work closely with business leaders and the senior science lead to answer critical questions: does targeting specific cohorts (Bargain hunters, Young adults) improve efficiency vs. broad campaigns? Which creative strategies drive behavior change? How should we optimize marketing spend across channels? Key job responsibilities Measurement & Experimentation Ownership: 1. Own measurement end-to-end for lifecycle marketing campaigns – design experiments (RCTs, geo-tests, audience holdouts) that measure campaign effectiveness across marketing channels 2. Build measurement frameworks and experimental best practices that work across different activation platforms and can scale to multiple campaigns 3. Establish experimental standards and tooling for lifecycle marketing, ensuring statistical rigor while balancing business constraints Causal Inference & Analysis: 1. Apply causal inference methods to measure incremental impact of marketing campaigns vs. counterfactual 2. Navigate measurement challenges across different platforms (Meta attribution, LiveRamp, clean rooms, onsite tracking) 3. Analyze experiment results and provide optimization recommendations based on statistical evidence 4. Establish guardrails and success criteria for campaign evaluation About the team The PRIMAS team, is part of a larger tech tech team of 100+ people called WIMSI (WW Integrated Marketing Systems and Intelligence). WIMSI core mission is to accelerate marketing technology capabilities that enable de-averaged customer experiences across the marketing funnel: awareness, consideration, and conversion.