Careers

Are you excited about building at the intersection of enterprise AI and customer experience? Are you passionate about applying science to real customer problems? Do you want your work shaped by real-world conditions and validated through customer deployments? The Alexa Enterprise team is looking for a passionate, talented, and inventive Senior Applied Scientist with a strong background in speech and audio machine learning who thrives in a startup-style environment. In this role, you'll set scientific direction and lead the development of techniques to improve speech-to-text accuracy for domain-specific use cases and optimize the performance and latency of end-to-end audio pipelines. You'll lead work across model evaluation and selection, fine-tuning, data and evaluation strategies, and real-time inference optimization. You'll partner closely with software engineers to turn scientific advances into production capabilities, while working with enterprise customers to understand how speech and audio systems perform in their environments. You'll turn improvements inspired by one customer's needs into capabilities that serve many, while influencing teams around a shared scientific vision. We're a small, fast-moving team building AI-powered solutions at the intersection of Alexa AI capabilities and AWS cloud services. Our customers span healthcare, energy, retail, and insurance, and they're deploying in environments where the technical challenges are real and the feedback loops are immediate. Key job responsibilities - Set the scientific direction for improving speech-to-text accuracy across domain-specific use cases and real-world operating conditions - Lead the evaluation, selection, adaptation, and fine-tuning of speech and audio models based on accuracy, latency, cost, reliability, and deployment constraints - Drive improvements to the performance and latency of end-to-end audio pipelines, from signal processing and streaming through inference and transcription - Define datasets, benchmarks, experiments, and evaluation methodologies that reflect customer use cases - Lead the optimization of models and inference pipelines for reliable, real-time operation - Work directly with enterprise customers to understand quality challenges, validate scientific improvements, and identify opportunities for broader platform capabilities - Turn patterns from customer engagements into reusable models, evaluation methods, and scientific capabilities that scale across customers and industries - Influence and collaborate with software engineering and partner teams to productionize models, measure their performance, and continuously improve deployed systems - Write scientific and technical documents, lead reviews, and communicate experimental results and trade-offs to engineering, product, and business leaders - Mentor Applied Scientists and engineers, participate in hiring, and raise the team's science and engineering standards About the team The Alexa Enterprise team builds AI-powered solutions for businesses. We work with enterprise customers across healthcare, energy, retail, and insurance to deploy solutions that transform operations. Our team operates at the intersection of Alexa AI capabilities and AWS cloud services, partnering closely with AWS sales, product, and specialist teams to deliver customer outcomes. Our work brings together applied AI, speech and audio science, real-time systems, and cloud services. Senior Applied Scientists on the team have real ownership, from identifying and framing customer problems through setting scientific direction, experimentation, production integration, and measurement of customer impact. This is an opportunity to lead meaningful scientific work while helping shape a platform in its early stages.

Are you excited about building at the intersection of enterprise AI and customer experience? Are you passionate about applying science to real customer problems? Do you want your work shaped by real-world conditions and validated through customer deployments? The Alexa Enterprise team is looking for a passionate, talented, and inventive Applied Scientist with a strong background in speech and audio machine learning who thrives in a startup-style environment. In this role, you’ll develop and apply scientific techniques to improve speech-to-text accuracy for domain-specific use cases and optimize the performance and latency of end-to-end audio pipelines. You’ll work across model evaluation and selection, fine-tuning, data and evaluation strategies, and real-time inference optimization. You’ll partner closely with software engineers to turn scientific advances into production capabilities, while working with enterprise customers to understand how speech and audio systems perform in their environments. The improvements inspired by one customer’s needs become capabilities that serve many. We’re a small, fast-moving team building AI-powered solutions at the intersection of Alexa AI capabilities and AWS cloud services. Our customers span healthcare, energy, retail, and insurance, and they’re deploying in environments where the technical challenges are real and the feedback loops are immediate. Key job responsibilities - Develop and apply modeling techniques to improve speech-to-text accuracy for domain-specific use cases and real-world operating conditions - Evaluate, select, adapt, and fine-tune speech and audio models based on accuracy, latency, cost, reliability, and deployment constraints - Improve the performance and latency of end-to-end audio pipelines, from signal processing and streaming through inference and transcription - Design datasets, benchmarks, experiments, and evaluation methodologies that reflect customer use cases - Optimize models and inference pipelines for reliable, real-time operation - Work directly with enterprise customers to understand quality challenges, validate scientific improvements, and identify opportunities for broader platform capabilities - Turn patterns from customer engagements into reusable models, evaluation methods, and scientific capabilities that scale across customers and industries - Collaborate with software engineers to productionize models, measure their performance, and continuously improve deployed systems - Write scientific and technical documents, lead reviews, and communicate experimental results and trade-offs to engineering, product, and business stakeholders - Contribute to the team’s scientific direction, mentor teammates, participate in hiring, and improve science and engineering processes About the team The Alexa Enterprise team builds AI-powered solutions for businesses. We work with enterprise customers across healthcare, energy, retail, and insurance to deploy solutions that transform operations. Our team operates at the intersection of Alexa AI capabilities and AWS cloud services, partnering closely with AWS sales, product, and specialist teams to deliver customer outcomes. Our work brings together applied AI, speech and audio science, real-time systems, and cloud services. Applied Scientists on the team have real ownership, from identifying and framing customer problems through experimentation, production integration, and measurement of customer impact. This is an opportunity to solve meaningful scientific problems while helping shape a platform in its early stages.

The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. We are looking for a Senior Applied Scientist to build the science that helps Amazon's advertisers grow — with a focus on Cross Border Sellers who face distinct barriers as they scale across multiple marketplaces, and this role is about understanding those pain points deeply and removing them: building intelligent, autonomous solutions that simplify advertising, act efficiently on the advertiser's behalf, and let advertisers accelerate their growth and success. We expect a Senior Scientist to think innovatively about how to reduce the effort and complexity of advertising for these advertisers. Working backwards from their needs — spanning hands-off sellers and global brands with cross-marketplace operations — you will take the lead on medium-to-large, ambiguous problems where neither the problem nor the solution is well defined, invent new methods, validate them through rigorous experimentation, and deliver customer-facing products with measurable business impact. This role combines science depth, product focus, and hands-on engineering: you will raise the science bar, build consensus on approach across product and engineering partners, and mentor scientists and engineers while remaining deeply hands-on with the hardest technical problems. Key job responsibilities As a Senior Applied Scientist on this team you will: - Understand the pain points of Cross border advertisers as they scale across marketplaces, and build science-driven solutions that remove barriers and accelerate their growth and success. - Build agentic and ML systems that autonomously create, structure, and manage ad campaigns on advertisers' behalf, encoding auction and marketplace dynamics (bidding, budget pacing, targeting decisions) while balancing advertiser ROI, shopper experience, and marketplace health. - Innovate on new, simpler ways to advertise powered by GenAI, and push the frontier of existing autonomous programs (e.g., auto-targeting, global lift-and-shift) while proposing and prototyping the next generation of campaign automation. - Develop models across the campaign lifecycle — opportunity discovery, ranking, ad-readiness and demand prediction, and bid/budget optimization — and apply the right approach for each problem, from classical ML to LLM/reasoning methods. - Define and curate the datasets and signals needed to train and evaluate these systems — advertiser and campaign data, cross-marketplace performance, auction and bid/budget signals, impressions, clicks, conversions, and search-term/keyword performance. - Own core parts of the agentic architecture — planning, tool use and integration (e.g., MCP), reasoning frameworks (e.g., ReAct, CoT/ToT), and model customization — and define evaluation and safety methodology so that automated decisions are reliable and trustworthy. - Stay deeply hands-on: write production-quality, critical-path code and build core components that take systems from prototype to launch on large-scale pipelines (Spark/EMR, Airflow) and online serving. - Raise the science bar: mentor scientists and engineers, review designs and experiment plans, and communicate results and tradeoffs clearly to technical and business leaders. About the team Autonomous SP drives growth and simplifies advertising for Amazon's hands-off advertisers by creating and enhancing autonomous campaign solutions. We lead existing successful programs, including auto-targeting and global lift-and-shift, and continually innovate by building new, simpler campaign constructs powered by GenAI to act efficiently on advertisers' behalf. Cross-border Seller Experience (CBSX) focuses on developing targeted solutions for domestic and global advertisers with multi-marketplace operations. By understanding and addressing their unique Seller Central needs, we optimize for cross-marketplace efficiency, improved performance, and streamlined advertising experiences. Our team operates horizontally, delivering impactful solutions that benefit both hands-on and hands-off advertisers globally. Together, we sit within Sponsored Products and Brands, which is re-imagining advertising through the latest generative AI — building responsible, intelligent systems that balance the needs of advertisers, the shopping experience, and marketplace health.

Amazon is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at an unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic dexterous manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. At Amazonwe leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at an unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. Key job responsibilities - Collaborate with simulation and robotics experts to translate physical modeling needs into robust, scalable, and maintainable simulation solutions. - Design and implement high-performance simulation modeling and tools for rigid and deformable body simulation. - Identify and optimize performance bottlenecks in simulation pipelines to support real-time and batch simulation workflows. - Help build validation and unit testing pipelines to ensure correctness and physical fidelity of simulation results. - Identify potential sources of sim-to-real gaps and propose modeling and numerical approximations to reduce them. - Stay current with the latest advances in numerical methods, parallel computing, and GPU architectures, and incorporate them into our tools.

robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. At Amazon Industrial Robotics we leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. We are pioneering the development of robotics foundation models that: Enable unprecedented generalization across diverse tasks Enable unprecedented robustness and reliability, industry-ready Integrate multi-modal learning capabilities (visual, tactile, linguistic) Accelerate skill acquisition through demonstration learning Enhance robotic perception and environmental understanding Streamline development processes through reusable capabilities The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. As an Applied Science Manager in the Foundation Model team, you will build and lead a team that develops and improves machine learning systems that help robots perceive, reason, and act in real-world environments. You will set the technical direction for leveraging state-of-the-art models (open source and internal research), evaluating them on representative tasks, and adapting/optimizing them to meet robustness, safety, and performance needs. You will drive the capability roadmap and the evaluation strategy that defines “what the robot brain can do,” and you will sponsor targeted innovation when gaps remain. You’ll collaborate closely with research, controls, hardware, and product teams, and ensure the team’s outputs can be further customized and deployed by downstream teams on specific robot embodiments.

The Data Intelligence team is a new function within Amazon Customer Service (CS). We own the end-to-end process of defining, building, implementing, and monitoring a comprehensive data strategy. We also develop and apply Generative Artificial Intelligence (GenAI), Machine Learning (ML), Ontology, and Natural Language Processing (NLP) to enhance customer service associate and customer experiences. As an Applied Scientist, you'll own the definition and implementation of customer-focused, AI-driven innovation in Amazon Customer Service globally, leveraging GenAI, ML, and/or NLP to transform complex business requirements and customer needs into innovative technology solutions. Your expertise will be key in shaping data-driven strategies and addressing complex data challenges. With your expertise in AI, text analysis, embeddings, language modeling, and generation, you'll design and develop scalable AI-powered technology solutions, prioritize initiatives, drive data-driven insights, and deliver business impact. This position will advance applied science best practices, leverage data and AI to drive customer experience improvements, and set new global standards for customer experience. This role requires you to work with a cross-functional team, including scientists, engineers, and product managers, to develop scalable and maintainable AI solutions for both structured and unstructured data. The ideal candidate has strong technical skills in AI techniques (e.g., automated reasoning, reasoning, planning, knowledge representation), excellent written documentation skills, and experience with big data technologies. Success in this role requires combining deep business knowledge with hands-on technical skills to solve customer problems and address complex technical challenges. Key job responsibilities - Develop innovative solutions to complex problems (e.g., Automated Reasoning for Trusted AI-Enabled Customer Service). - Apply technical expertise to implement novel algorithms and modeling solutions, in collaboration with other scientists and engineers. - Analyze data and define metrics to identify actionable insights and measure improvements in customer experience. - Communicate results and insights to both technical and non-technical audiences through written reports, presentations, and internal/external publications. - Collaborate with product management and engineering teams to integrate and optimize models in production systems. A day in the life A typical day as an Applied Scientist in the Data Intelligence team involves combining business expertise with hands-on problem-solving in ML and AI. The role encompasses tackling complex data initiatives, ensuring alignment with customer needs and business objectives, and translating business requirements into practical AI-driven solutions. Working collaboratively with cross-functional teams, this position involves designing and enhancing AI models, focusing on efficiency, precision, and scalability. Daily activities include ensuring data quality, monitoring model performance, and generating actionable insights from vast amounts of information. Each day presents opportunities to resolve complex technical challenges, advance important AI projects, and conceive innovative ways to leverage data in transforming the customer experience. About the team The Data Intelligence team is a new function within Amazon Customer Service. We develop and apply Generative Artificial Intelligence (GenAI), Machine Learning (ML), and Natural Language Processing (NLP) techniques to enhance customer service associate and customer experiences.

Do you want to create the greatest-possible worldwide impact in Robotics? Amazon has the world's most exciting treasure trove of robotics challenges. At Amazon Robotics we build high-performance, real-time robotic systems that can perceive, learn, and act intelligently alongside humans—at Amazon scale. Amazon Robotics invents and scales AI systems for robotics in fulfillment. Our mission is to enable robots to interact safely, efficiently, and fluently high density real-world fulfillment centers. Our AI solutions enable robots to learn from their own experiences, from each other, and from humans to build intelligence that feeds itself. We hire and develop collaborative subject matter experts in AI with a focus on computer vision, deep learning, semi-supervised and unsupervised learning. We target high-impact algorithmic unlocks in areas such as scene and activity understanding, large scale generative models, closed-loop control, robotic grasping and manipulation, all of which have high-value impact for our current and future fulfillment networks. We are seeking a hands-on, seasoned Applied Scientists who will be deep in code and algorithms; who are technically strong in building scalable vision systems across item understanding, pose estimation, multi-view scene completion, class imbalanced classifiers, identification and segmentation. As a Applied Scientist, you will contribute to the research and development of advanced robotic systems; your work along with other top-notch scientists and engineers will deliver the world's most scalable and robust robotic systems. You will drive ideas to products using paradigms such as deep learning, semi supervised learning and active learning. As a Applied Scientist, you will also help lead and mentor our team of applied scientists and engineers. You will take on challenging customer problems, distill customer requirements, and then deliver solutions that either leverage existing academic and industrial research or utilize your own out-of-the-box but pragmatic thinking. In addition to coming up with novel solutions and prototypes, you will directly contribute to implementation while you lead. A successful candidate has excellent technical depth, scientific vision, project management skills, great communication skills, and a drive to achieve results in a collaborative team environment. You should enjoy the process of solving real-world problems that, quite frankly, haven’t been solved at scale anywhere before. Along the way, we guarantee you’ll get opportunities to be a disruptor, prolific innovator, and a reputed problem solver, someone who truly enables AI and robotics to significantly impact the lives of millions of consumers. Key job responsibilities - Architect, design, and implement Machine Learning models for vision systems on robotic platforms - Optimize, deploy, and support at scale ML models on the edge. Influence the team's strategy and contribute to long-term vision and roadmap. - Work with stakeholders across , science, and operations teams to iterate on design and implementation. - Maintain high standards by participating in reviews, designing for fault tolerance and operational excellence, and creating mechanisms for continuous improvement. - Prototype and test concepts or features, both through simulation and emulators and with live robotic equipment - Work directly with customers and partners to test prototypes and incorporate feedback - Mentor other engineer team members. A day in the life Amazon offers a full range of benefits for you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply! About the team https://www.youtube.com/watch?v=2X4CU3jmw-g The Vulcan Stow Perception team builds the visual intelligence that enables Amazon's next-generation robotic stow systems to understand and interact with densely packed fulfillment environments. We own the full perception stack, from raw sensor input to actionable 3D scene representations, powering robots that autonomously stow millions of items daily across Amazon's global network. Our team tackles some of the hardest unsolved problems in 3D robotic perception: completing occluded scenes from partial observations, generating real-time semantic occupancy predictions, fusing multi-camera inputs (pedestal and end-of-arm tool), and producing sub-250ms mesh reconstructions that drive downstream manipulation decisions. We operate at the intersection of research and production-scale deployment, building systems that must be both scientifically rigorous and operationally bulletproof. We are a tight-knit group of applied scientists and engineers who ship models that run on real robots in real fulfillment centers, not just papers or prototypes. Our culture values technical depth, rapid experimentation, and end-to-end ownership. If you want to push the boundaries of 3D computer vision, work with transformer and generative architectures at the frontier, and see your work directly impact how millions of packages reach customers - this is the team.

The Central Science Team within Amazon’s People Experience and Technology org (PXTCS) uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, well-being, and the value of work to Amazonians. We are an interdisciplinary team, which combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal. We are looking for a Senior Economist who is able to provide structure around complex business problems, hone those complex problems into specific, scientific questions, and test those questions to generate insights. The ideal candidate will work with various science, engineering, operations, and analytics teams to estimate models and algorithms on large scale data, design pilots and measure their impact, and transform successful prototypes into improved policies and programs at scale. They will lead teams of researchers to produce robust, objective research results and insights which can be communicated to a broad audience inside and outside of Amazon. The ideal candidate has a PhD in Economics and deep expertise in causal inference and applied econometrics. Experience with large-scale data, proficiency in statistical programming (Python), and familiarity with machine learning methods are a plus. To be successful in this role, you should be comfortable operating with ambiguity, able to independently scope and prioritize research agendas, skilled at influencing decisions through rigorous analysis, and comfortable with using AI tools.

Are you passionate about giving customers the richest, most inspiring experience in their shopping journey? Do you like to dive deep to understand how customer-centric solutions drive measurable results? Do you enjoy working closely with the business and software engineers to design rigorous experiments, build the data infrastructure behind them, and translate results into decisions? You are in the right place! Come join our Prime & Marketing Analytics and Science (PRIMAS) team, where your work will directly impact millions of customers. The EU Marketing & Prime organization is looking for a Data Scientist to join the PRIMAS team. This role sits at the intersection of applied statistics and large-scale analytics — you'll design experiments and causal models, and also own the data pipelines, metrics, and reporting infrastructure that make those results usable across the business. The PRIMAS team provides a comprehensive understanding of customer segments, affinities, and lifetime value. We use data science tools and advanced statistical techniques to study customer purchase and engagement behaviors, and generate actionable insights on where, when, and how we deliver products and programs to customers. We help increase customer engagement, sales, and marketing efficiency, and our systems are built entirely in-house on automated large-scale analytics infrastructure. You will design, launch, and measure experiments across marketing channels (SEM/SEO, Affiliates, Display, Social, Mobile, Email, Onsite, etc.), engagement products, and customer segments. You will improve our understanding of customer behavior, run rigorous power and minimum detectable effect (MDE) analyses to size experiments correctly, and build the causal and conversion models that value and target our marketing — then build the pipelines and dashboards that keep those signals flowing reliably to stakeholders and downstream systems. You will work at the forefront of consumer analytics, tackling some of the hardest measurement problems in the industry alongside strong scientists, statisticians, and software engineers. Key job responsibilities 1. Design and implement scalable, statistically rigorous experiments (A/B, geo, holdout, quasi-experiments) to measure marketing incrementality across channels. 2. Perform power analysis and minimum detectable effect (MDE) calculations to determine experiment sample sizes, durations, and design trade-offs before launch. 3. Build causal and treatment-effect models that produce conversion and valuation signals consumed by downstream bidding and budgeting systems. 4. Building the ETL, metric definitions, and datasets that make results scalable, extensible, and repeatable rather than one-off analyses. 5. Develop measurement frameworks that quantify the true, platform-independent contribution of marketing over time, and build the dashboards and reporting that keep those metrics visible to the business. 6. Apply statistical, mathematical, and machine learning techniques to solve ambiguous business problems where the right approach isn't obvious. 7. Analyze experiment results for validity — inspecting distributions, checking for sample ratio mismatch, exploring covariate balance, and tracking down the source of anomalies. 8. Communicate experiment design, results, and trade-offs clearly to business and leadership audiences, including inputs into business reviews, and influence decisions and technical direction across teams. 9. Establish scalable, repeatable processes and best practices for experiment design, data modeling, and analysis.

Are you passionate about giving customers the richest, most inspiring experience in their shopping journey? Do you like to dive deep to understand how customer-centric solutions drive measurable results? Do you enjoy working closely with the business and software engineers to design rigorous experiments, build the data infrastructure behind them, and translate results into decisions? You are in the right place! Come join our Prime & Marketing Analytics and Science (PRIMAS) team, where your work will directly impact millions of customers. The EU Marketing & Prime organization is looking for a Data Scientist to join the PRIMAS team. This role sits at the intersection of applied statistics and large-scale analytics — you'll design experiments and causal models, and also own the data pipelines, metrics, and reporting infrastructure that make those results usable across the business. The PRIMAS team provides a comprehensive understanding of customer segments, affinities, and lifetime value. We use data science tools and advanced statistical techniques to study customer purchase and engagement behaviors, and generate actionable insights on where, when, and how we deliver products and programs to customers. We help increase customer engagement, sales, and marketing efficiency, and our systems are built entirely in-house on automated large-scale analytics infrastructure. You will design, launch, and measure experiments across marketing channels (SEM/SEO, Affiliates, Display, Social, Mobile, Email, Onsite, etc.), engagement products, and customer segments. You will improve our understanding of customer behavior, run rigorous power and minimum detectable effect (MDE) analyses to size experiments correctly, and build the causal and conversion models that value and target our marketing — then build the pipelines and dashboards that keep those signals flowing reliably to stakeholders and downstream systems. You will work at the forefront of consumer analytics, tackling some of the hardest measurement problems in the industry alongside strong scientists, statisticians, and software engineers. Key job responsibilities 1. Design and implement scalable, statistically rigorous experiments (A/B, geo, holdout, quasi-experiments) to measure marketing incrementality across channels. 2. Perform power analysis and minimum detectable effect (MDE) calculations to determine experiment sample sizes, durations, and design trade-offs before launch. 3. Build causal and treatment-effect models that produce conversion and valuation signals consumed by downstream bidding and budgeting systems. 4. Building the ETL, metric definitions, and datasets that make results scalable, extensible, and repeatable rather than one-off analyses. 5. Develop measurement frameworks that quantify the true, platform-independent contribution of marketing over time, and build the dashboards and reporting that keep those metrics visible to the business. 6. Apply statistical, mathematical, and machine learning techniques to solve ambiguous business problems where the right approach isn't obvious. 7. Analyze experiment results for validity — inspecting distributions, checking for sample ratio mismatch, exploring covariate balance, and tracking down the source of anomalies. 8. Communicate experiment design, results, and trade-offs clearly to business and leadership audiences, including inputs into business reviews, and influence decisions and technical direction across teams. 9. Establish scalable, repeatable processes and best practices for experiment design, data modeling, and analysis.