Econometrics

AWS AI is looking for passionate, talented, and inventive Applied Scientists with a strong machine learning background to help build industry-leading Conversational AI Systems. Our mission is to provide a delightful experience to Amazon’s customers by pushing the envelope in Natural Language Understanding (NLU), Dialog Systems including Generative AI with Large Language Models (LLMs) and Applied Machine Learning (ML). As part of our AI team in Amazon AWS, you will work alongside internationally recognized experts to develop novel algorithms and modeling techniques to advance the state-of-the-art in human language technology. Your work will directly impact millions of our customers in the form of products and services that make use language technology. You will gain hands on experience with Amazon’s heterogeneous text, structured data sources, and large-scale computing resources to accelerate advances in language understanding. We are hiring in all areas of human language technology: NLU, Dialog Management, Conversational AI, LLMs and Generative AI. 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. Hybrid Work We value innovation and recognize this sometimes requires uninterrupted time to focus on a build. We also value in-person collaboration and time spent face-to-face. Our team affords employees options to work in the office every day or in a flexible, hybrid work model near one of our U.S. Amazon offices.

An information-rich and accurate product catalog is a strategic asset for Amazon. It powers unrivaled product discovery, informs customer buying decisions, offers a large selection, and positions Amazon as the first stop for shopping online. We use data analysis and statistical and machine learning techniques to proactively identify relationships between products within the Amazon product catalog. This problem is challenging due to sheer scale (billions of products in the catalog), diversity (products ranging from electronics to groceries to instant video across multiple languages) and multitude of input sources (millions of sellers contributing product data with different quality). Amazon’s Item and Relationship Identity Systems group is looking for an innovative and customer-focused applied scientist to help us make the world’s best product catalog even better. We believe that failure and innovation are inseparable twins. In this role, you will partner with technology and business leaders to build new state-of-the-art algorithms, models, and services to infer product-to-product relationships that matter to our customers. You will work in a collaborative environment where you can experiment with massive data from the world’s largest product catalog, work on challenging problems, quickly implement and deploy your algorithmic ideas at scale, understand whether they succeed via statistically relevant experiments across millions of customers. Key job responsibilities * Map business requirements and customer needs to a scientific problem. * Align the research direction to business requirements and make the right judgments on research/development schedule and prioritization. * Research, design and implement scalable machine learning (ML), natural language, or computational models to solve problems that matter to our customers in an iterative fashion. * Mentor and develop junior applied scientists and developers who work on data science problems in the same organization. * Stay informed on the latest machine learning, natural language and/or artificial intelligence trends and make presentations to the larger engineering and applied science communities.

Are you passionate about automation, knowledge extraction, and artificial intelligence through the use of Machine Learning, Natural Language Processing, Recommender systems, Computer Vision, and Optimization? We have a team of experienced scientists with a critical business mission making revolutionary leaps forward in these spaces. On this team you will work with an immense and diverse corpus of text, image, and audio to build generative and discriminative models, analyze and model customer reading behavior to measure engagement and detect risks, study and optimize manufacturing and fulfillment processes, and build AI-based systems for helping indie authors with marketing their books. This will involve combining methods from several science domains with domain knowledge across multiple businesses into sophisticated ML workflows. Our team has mature areas and green-field opportunities. We offer scientific autonomy, value end-to-end ownership, and have a strong customer-focused culture. Come join us as we revolutionize the book industry and deliver an amazing experience to our Kindle authors and readers. Key job responsibilities As a Machine Learning Scientist at Amazon, you will connect with world leaders in your field working on similar problems. You will be working with large distributed systems of data and providing technical leadership to the product managers, teams, and organizations building machine learning solutions. You will be tackling Machine Learning challenges in Supervised, Unsupervised, and Semi-supervised Learning; utilizing modern methods such as deep learning and classical methods from statistical learning theory, detection, estimation. MLS’s are specialists with the knowledge to help drive the scientific vision for our products. They are externally aware of the state-of-the-art in their respective field of expertise and are constantly focused on advancing that state-of-the-art for improving Amazon’s products and services. Great candidates for this position will have experience in the areas of data science, machine learning, NLP, optimization, computer vision, or statistics. You will have hands-on experience with multiple science initiatives as well as be able to balance technical strength with business judgment to make decisions about technology, models and methodological choices. You will strive for simplicity, and demonstrate significant creativity and high judgment. About the team Kindle Direct Publishing (KDP) and Print-On-Demand (POD) have empowered a new wave of self-motivated creators, tearing down barriers that once blocked writers from reaching readers. Our team builds rich applications that empower anyone to realize their dream of becoming an author. We strive to provide an experience that is powerful, simple, and accessible to all. We build tools that enable authors to design high quality digital and print books, reaching readers all around the world. This role will help ensure we maintain the trust of both our Authors and Readers by ensuring all books published to Amazon meet our standards.

The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background, to help build industry-leading technology with multimodal systems. Key job responsibilities As an Applied Scientist with the AGI team, you will work with talented peers to develop novel algorithms and modeling techniques to advance the state of the art with multimodal systems. Your work will directly impact our customers in the form of products and services that make use of vision and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate development with multimodal Large Language Models (LLMs) and Generative Artificial Intelligence (GenAI) in Computer Vision. About the team The AGI team has a mission to push the envelope with multimodal LLMs and GenAI in Computer Vision, in order to provide the best-possible experience for our customers.

Do you want to work on a team where you are encouraged to build and have the autonomy to push boundaries? Invention has become second nature at Amazon, and the pace of innovation is only accelerating with breadth of our businesses expanding. Amazon’s growth requires leaders who move fast, have an entrepreneurial spirit to create new products, have an unrelenting tenacity to get things done, and are capable of breaking down and solving complex problems. The AIM, Planning team within SCOT comprises of S&OP, Inventory Prediction and Entitlement and Long-Term Capacity and Topology Planning. The team's charter is broad and complex and aimed at optimizing the utilization of fulfillment facilities and resources by accurately predicting demand and inventory efficiency measures while reducing stockouts and excess inventory costs across planning horizons, from short-term (within 13 weeks) to the long-term (13 weeks to 5 years). The team's north star is to be the reliable, single source of truth for inventory units and cube demand at granularities ranging from an FC’s bins to overall network level, and across planning horizons as close as next week to as far out as 3-5 years. To get there, we enhance or re-develop models and mechanisms where existing ones fail to account for structural shifts in supply chains, buying programs, or customer behaviors. We create new systems where science-based recommendations are currently lacking and being replaced by heuristics and offline human goal-seeking approaches. We strive to completely eliminate non-scientific interventions in our forecast guidance and capacity recommendations, and replace them with a system-driven outlook to uncover underlying root causes when departing from SCOT plans and recommendations. We institute authoritative and economics-based framework missing today to drive inventory efficiency measures for Retail buying programs (short/long-lead buys) and FBA plans that solve for capacity constraints in the most economical manner across horizons. This is a unique, high visibility opportunity for a senior science leader someone who wants to have business impact, dive deep into large-scale economic problems, enable measurable actions on the Consumer economy, and work closely with product managers, engineers, other scientists and economists. We are a Day 1 team, with a charter to be disruptive through the use of ML and bridge the Science and Engineering gaps that exist today. A day in the life In this pivotal role, you will be a technical leader in operations research or machine learning, with significant scope, impact, and visibility. Your solutions have the potential to drive billions of dollars in impact for Amazon's supply chain globally. As a senior scientist manager on the team, you will engage in every facet of the process—from idea generation, business analysis and scientific research to development and deployment of advanced models—granting you a profound sense of ownership. From day one, you will collaborate with experienced scientists, engineers, and product managers who are passionate about their work. Moreover, you will collaborate with Amazon's broader decision and research science community, enriching your perspective and mentoring fellow engineers and scientists. The successful candidate will have the strong expertise in applying operations research methodologies to address a wide variety of supply chain problems. You will strive for simplicity, demonstrate judgment backed by mathematical rigor, as you continually seek opportunities to innovate, build, and deliver. Entrepreneurial spirit, adaptability to diverse roles, and agility in a fast-paced, high-energy, highly collaborative environment are essential.

We are a part of Amazon Alexa organization where our mission is “delight customers through contextual and personalized proactive experiences that keep customers informed, engaged, and productive without cognitive burden”. We are developing advanced systems to deliver engaging, intuitive, and adaptive content recommendations across all Amazon surfaces. We aim to facilitate seamless reasoning and customer experiences, surpassing the capabilities of previous machine learning models. We are looking for a passionate, talented, and resourceful Senior Applied Scientist in the field of Natural Language Processing (NLP), Large Language Model (LLM), Recommender Systems and/or Information Retrieval, to invent and build scalable solutions for a state-of-the-art context-aware personal assistant. A successful candidate will have strong machine learning background and a desire to push the envelope in one or more of the above areas. The ideal candidate would also enjoy operating in dynamic environments, be self-motivated to take on challenging problems to deliver big customer impact, shipping solutions via rapid experimentation and then iterating on user feedback and interactions. Key job responsibilities As a Senior Applied Scientist, you will leverage your technical expertise and experience to demonstrate leadership in tackling large complex problems, setting the direction and collaborating with applied scientists and engineers to develop novel algorithms and modeling techniques to enable timely, relevant and delightful recommendations and conversations. Your work will directly impact our customers in the form of products and services that make use of various machine learing, deep learning and language model technologies. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in the state of art.

Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by preventing eCommerce fraud? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you enjoy collaborating in a diverse team environment? If yes, then you may be a great fit to join the Amazon Buyer Risk Prevention (BRP) Machine Learning group. We are looking for a talented scientist who is passionate to build advanced algorithmic systems that help manage safety of millions of transactions every day. Key job responsibilities Use machine learning and statistical techniques to create scalable risk management systems Learning and understanding large amounts of Amazon’s historical business data for specific instances of risk or broader risk trends Design, development and evaluation of highly innovative models for risk management Working closely with software engineering teams to drive real-time model implementations and new feature creations Working closely with operations staff to optimize risk management operations, Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation Tracking general business activity and providing clear, compelling management reporting on a regular basis Research and implement novel machine learning and statistical approaches

Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by preventing eCommerce fraud? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you enjoy collaborating in a diverse team environment? If yes, then you may be a great fit to join the Amazon Buyer Risk Prevention (BRP) Machine Learning group. We are looking for a talented scientist who is passionate to build advanced algorithmic systems that help manage safety of millions of transactions every day. Key job responsibilities Use machine learning and statistical techniques to create scalable risk management systems Learning and understanding large amounts of Amazon’s historical business data for specific instances of risk or broader risk trends Design, development and evaluation of highly innovative models for risk management Working closely with software engineering teams to drive real-time model implementations and new feature creations Working closely with operations staff to optimize risk management operations, Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation Tracking general business activity and providing clear, compelling management reporting on a regular basis Research and implement novel machine learning and statistical approaches

We are building GenAI based shopping assistant for Amazon. We reimage Amazon Search with an interactive conversational experience that helps you find answers to product questions, perform product comparisons, receive personalized product suggestions, and so much more, to easily find the perfect product for your needs. We’re looking for the best and brightest across Amazon to help us realize and deliver this vision to our customers right away. This will be a once in a generation transformation for Search, just like the Mosaic browser made the Internet easier to engage with three decades ago. If you missed the 90s—WWW, Mosaic, and the founding of Amazon and Google—you don’t want to miss this opportunity.

We are building GenAI based shopping assistant for Amazon. We reimage Amazon Search with an interactive conversational experience that helps you find answers to product questions, perform product comparisons, receive personalized product suggestions, and so much more, to easily find the perfect product for your needs. We’re looking for the best and brightest across Amazon to help us realize and deliver this vision to our customers right away. This will be a once in a generation transformation for Search, just like the Mosaic browser made the Internet easier to engage with three decades ago. If you missed the 90s—WWW, Mosaic, and the founding of Amazon and Google—you don’t want to miss this opportunity.