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Economics

Developing sophisticated approaches and systems to deliver the broadest selection of products and services at the lowest prices.

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  • Yao Zhao, Kwang-Sung Jun, Tanner Fiez, Lalit Jain
    2023 Conference on Digital Experimentation @ MIT (CODE@MIT), NeurIPS 2024
    2024
    This paper introduces the confounded pure exploration transductive linear bandit (CPET-LB) problem. As a motivating example, often online services cannot directly assign users to specific control or treatment experiences either for business or practical reasons. In these settings, naively comparing treatment and control groups that may result from self-selection can lead to biased estimates of underlying
  • Paula Meloni, Stefan Hut, Mahnaz Islam
    2024 Conference on Digital Experimentation @ MIT (CODE@MIT)
    2024
    There are different reasons why experimenters may want to randomize their experiment at a region level. In some cases, treatments cannot be turned on or off at the individual level, therefore requiring randomization at a group level, for which regions can be a good candidate. In other cases, experimenters may worry about network effects or other types of spillovers within a geographic area, and opt to randomize
  • 2024 Conference on Digital Experimentation @ MIT (CODE@MIT)
    2024
    Online sites typically evaluate the impact of new product features on customer behavior using online controlled experiments (or A/B tests). For many business applications, it is important to detect heterogeneity in these experiments [1], as new features often have a differential impact by customer segment, product group, and other variables. Understanding heterogeneity can provide key insights into causal
  • 2024 Conference on Digital Experimentation @ MIT (CODE@MIT)
    2024
    Many data-driven companies measure the impact of product groups and allocate resources across them based 2 on the estimated impacts of features they launch via A/B tests. In this doc, we show that, when based on a standard 3 frequentist estimator of the impact of features, this practice can significantly overstate the impact of product groups and 4 distort the allocation of resources. When this practice
  • Mark Howison, Will Ensor, Suraj Maharjan, Rahil Parikh, Srinivasan Sengamedu, "SHS", Paul Daniels, Amber Gaither, Carrie Yeats, Chandan Reddy, Justine Hastings
    ACM Digital Government Research and Practice (DGOV)
    2024
    Labor market information is an important input to labor, workforce, education, and macroeconomic policy. However, granular and real-time data on labor market trends are lacking; publicly available data from survey samples are released with significant lags and miss critical information such as skills and benefits. We use generative Artificial Intelligence to automatically extract structured labor market

Related content

US, WA, Seattle
We are hiring a Senior Economist with the ability to disambiguate very challenging structural problems in two and multi-sided markets. The right hire will be able to get dirty with the data to come up with stylized facts, build reduced form model that motivate structural assumptions, and build to more complex structural models. The main use case will be understanding the incremental effects of subsidies to a two sided market relate to sales motions characterized by principal agent problems. Key job responsibilities This role with interface directly with product owners, scientists/economists, and leadership to create multi-year research agendas that drive step change growth for the business. This role is important for development of the strategic direction of the AWS Central Economics and Science team. The role will also be an important collaborator with other science teams at AWS. A day in the life Our team takes big swings and works on hard cross organizational problems where the optimal success rate is not 100%. We also ask people to grow their skills and stretch and make sure we do it in a supportive and fun environment. It’s about empirically measured impact, advancement, and fun on our team. We work hard during work hours but we also don’t encourage working at nights or on weekends except in very rare, high stakes cases. Burn out isn’t a successful long run strategy. Because we invest in the long run success of our group it’s important to have hobbies, relax and then come to work refreshed and excited. It makes for bigger impact, faster skill accrual and thus career advancement. About the team Our group is technically rigorous and encourages ongoing academic conference participation and publication. Our leaders are here for you and to enable you to be successful. We believe in being servant leaders focused on influence: good data work has little value if it doesn’t translate into actionable insights that are rolled out and impact the real economy. We are communication centric since being able to explain what we do ensures high success rates and lowers administrative churn. Also: we laugh a lot. If it’s not fun, what’s the point?
US, WA, Seattle
We are working back from a mission of explaining and predicting one of the most important inputs to the Amazon business - customer visit data. We are working to model and test our assumptions about what are the customer incentives and what influences customers to chose visiting Amazon. Ultimately we want to understand and predict how many of our customers will interact with Amazon and we want to identify insights that optimize the customer experience. This is a green-field role in an analytics team of economists, science, business intelligence and data engineering bridging between observational measurement and theoretical models. About the team You will be empowered by a large team of experienced software and data engineers that are aligned on the same mission - to measure and explain our customer experience. We lean into the exceptionally talented Economist and Science community in CBA and Stores for consultation, guidance and peer reviews. Our large analytics team is empowered to move fast and gather the data we need to achieve our mission. Our parent organization owns end-to-end data collection systems (Clickstream), experimentation (Weblab) and customer value forecasting (GCCP). We are working across organizational boundaries to identify relevant datasets and are able to curate the vast amount of data we have into meaningful business reporting and analysis.
US, WA, Seattle
We are seeking a scientist with rigorous training in A/B testing and observational causal modeling (e.g. synthetic controls, causal matrix completion). Our team owns "causal inference as a service" for the Pricing and Promotions organization; we run A/B tests on new pricing, promotions, and pricing/promotions CX algorithms and, where experimentation is impractical, conduct observational causal studies. Key job responsibilities We are seeking a scientist excited to build the next generation of pricing, promotions, and pricing/promotions CX for Amazon. On our team, you will work at the intersection of economic theory, statistical inference, and machine learning to design and implement in production new statistical methods for measuring causal effects of an extensive array of business policies. This position is perfect for someone who has a deep and broad analytic background, is passionate about using mathematical modeling and statistical analysis to make a real difference. You should be familiar with modern tools for data science and business analysis and have an interest in working with engineers to put projects into production. We are particularly interested in candidates with research background in experimental statistics. A day in the life A day in the life Discuss with business problems with business partners, product managers, and tech leaders Brainstorm with other scientists to design the right model for the problem at hand Present the results and new ideas for existing or forward looking problems to leadership Dive deep into the data Build working prototypes of models Work with engineers to implement prototypes in production Analyze the results and review with partners About the team We are a team of scientists who design and implement the econometrics powering pricing, promotions, and pricing/promotions CX.
US, WA, Seattle
Amazon's Global XCM organization (Cross-Category, Cross-Channel Marketing) is looking for a talented Tech Lead Senior Economist who is interested in solving one of the most challenging business problems in marketing measurement and optimization, and developing cutting-edge ML model. Working with our team of data scientists, applied scientists, research scientists, and economists, this leader will help redefine scalable marketing measurement and optimization. XCM’s mission is to be the most culturally and contextually aware, creatively breakthrough, and measurably effective marketing organization in the world. In this role, you will be a technical leader in Econometric research with significant scope, impact, and high visibility. You will lead strategic measurement and optimization science initiatives in collaboration with external science teams, and development and deployment of measurement and real-time predictive and optimization science models. As a successful Economist, you are an analytical problem solver who enjoys diving into data, leads problem solving, guides development of new frameworks, writes code, is excited about investigations and algorithms, and can credibly interface between technical teams and business stakeholders. You are an expert in causal inference models to solve business problems. You are a hands-on innovator who can contribute to advancing Marketing measurement technology and push the limits on what’s scientifically possible with a razor sharp focus on measurable customer and business impact. You will also coach and guide junior scientists in the team to grow the team’s talent and scale the impact of your work. Key job responsibilities - Design and implement production-ready causal machine learning models to solve complex measurement and optimization challenges - Partner with cross-functional science teams to drive innovative high-impact initiatives - Collaborate with key stakeholders to develop strategic roadmaps and ensure successful project execution - Mentor junior scientists and establish technical best practices for the team
US, WA, Seattle
Amazon's Global XCM organization (Cross-Category, Cross-Channel Marketing) is looking for a talented Economist who is interested in solving one of the most challenging business problems in marketing measurement and optimization, and developing cutting-edge ML model. XCM’s mission is to be the most culturally and contextually aware, creatively breakthrough, and measurably effective marketing organization in the world. In this role, you will pioneer real-time marketing measurement and optimization solutions by combining causal inference with advanced ML models. You'll leverage Amazon's customer behavior data and external datasets to build scalable solutions, while partnering with marketing stakeholders and product teams to translate business needs into actionable insights. Your expertise in causal inference and machine learning will be crucial in enhancing customer engagement and marketing effectiveness across Amazon's global marketing ecosystem. Key job responsibilities - Design and implement advanced causal inference and machine learning solutions for marketing measurement and optimization; - Transform complex business requirements into technical questions - Translate model outputs to actionable insights through close collaboration with marketing stakeholders and product teams; - Work with product and engineering teams to deploy the model into automation system
US, WA, Seattle
The Selling Partner Fees team owns the end-to-end fees experience for two million active third party sellers. We own the fee strategy, fee seller experience, fee accuracy and integrity, fee science and analytics, and we provide scalable technology to monetize all services available to third-party sellers. We are looking for a Senior Economist to lead the development of economic models and tools to optimize the structure and magnitude of fees paid by >4MM of heterogeneous Sellers Worldwide. This person will work on the development of economic, statistical and machine learning models to understand third party seller behaviors and enhance our fee optimization models. The ideal candidate needs to be comfortable transitioning between theoretical Economic models and empirical ones, and use the combination of these models to provide guidance for fee setting. A successful Economist in this role will enjoy dealing with ambiguous problems and working in a fast-paced and dynamic environment. The position also requires collaboration with other scientists (Economists, Data Scientists…), Strategy Analysts, Product Managers and Software Developers. Key job responsibilities - Design and develop theoretical and empirical models to assess the causal impact of fees on third party sellers’ behavior and business performance. - Lead enhancements into existing fee calculation models to maximize the long term health of the Amazon third-party marketplace. - Collaborate with product managers, data scientists, and software developers to incorporate models into production processes and influence senior leaders. - Act as ambassador of the fees scientific community.
US, WA, Bellevue
The Fulfillment by Amazon (FBA) enable third-party sellers to use Amazon’s world-class science and logistics infrastructure to supply and fulfill customers worldwide with unprecedented fast delivery promise to customer. In doing so, sellers spend more time building great products, delight customers and grow their business. The FBA team is looking for an Economist intern with strong causal inference and econometrics skills to join our cross-domain group of economists, applied scientists, research scientists, and data scientists. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Some knowledge of econometrics, as well as basic familiarity with Python is necessary, and experience with SQL would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. Roughly 85% of previous cohorts have converted to full time economist employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com.
US, WA, Seattle
At Amazon, we're committed to pioneering new frontiers in customer experience, and Fashion Tech is at the forefront of this mission. Our programs and technologies are revolutionizing how customers interact with fashion products, presenting unique challenges and opportunities for quantifying their economic impact. We're seeking a highly experienced Economist with expertise in causal modeling to lead our efforts in understanding the economic impact of our efforts in this dynamic space. Over the years, teams across Amazon have built systems that can value content and even optimize what is shown based on relevant ‘value’ metrics. However, understanding attribution and program incrementality continue to be challenges that require tying the business context with the most relevant methodology from an array of possible ones. The person in this role will work with finance, CBA and business owners to define the right metrics and methodologies to compute attributed and incremental value of programs and features, while leveraging existing frameworks wherever applicable. We want to answer questions like “If this program didn’t exist, what is the total economic value to Amazon that we would stand to lose?” and “What is the ongoing business impact of this CX post-launch?”. Key job responsibilities - Spearhead collaborative efforts with finance, CBA (Cost-Benefit Analysis), and business teams to define robust metrics and methodologies for measuring the attributed and incremental value of Fashion Tech programs and features. - Develop advanced frameworks and models to assess the causal economic impact of content, programs, and customer experience enhancements throughout the customer purchase funnel. - Lead the resolution of complex challenges related to content attribution and program incrementality, leveraging existing systems and methodologies while exploring innovative approaches where necessary. - Conduct in-depth economic analyses to address critical business questions, such as estimating the economic value of key programs and evaluating the ongoing business impact of post-launch customer experience improvements. - Drive cross-functional collaboration with data scientists, economists, and business leaders to integrate economic insights into strategic decision-making processes and shape future initiatives. - Stay at the forefront of industry trends, economic research, and best practices in causal modeling and econometric techniques, continuously enhancing our methodologies and frameworks to ensure relevance and effectiveness. About the team The Fashion Tech organization has a mission to make Amazon the most-loved fashion destination globally through technology by building novel experiences that bring a diverse breadth of customers to shop fashion in the Amazon store. The Fashion Intelligence team improves the speed, accuracy, and standards of data driven decisions across all programs within Fashion Tech. As a central analytics team, our goal is to break silos and identify interconnectedness across Fashion Tech programs, keeping the customer at the center. Here at Fashion & Fitness, we are inspired to never stop embracing our uniqueness for both our employees and our customers.
US, WA, Seattle
The Selling Partner Fees team owns the end-to-end fees experience for two million active third party sellers. We own the fee strategy, fee seller experience, fee accuracy and integrity, fee science and analytics, and we provide scalable technology to monetize all services available to third-party sellers. We are looking for a Senior Economist to lead the development of economic models and tools to optimize the structure and magnitude of fees paid by >4MM of heterogeneous Sellers Worldwide. The ideal candidate will be an influencer with technical skills and a practical mindset. They will lead the development of economic, statistical and machine learning models to understand third party seller behaviors and enhance our fee optimization models. This candidate will be comfortable leading research in unexplored areas and communicating their findings with senior leadership. They will enjoy dealing with ambiguous problems and working in a fast-paced and dynamic environment. The position also requires collaboration with other scientists (Economists, Data Scientists…), Strategy Analysts, Product Managers and Software Developers. Responsibilities: · Design and develop rigorous models to assess the causal impact of fees on third party sellers’ behavior and business performance. · Lead enhancements into existing fee calculation models to maximize the long term health of the Amazon third-party marketplace. · Own the scientific vision and direction related to fees worldwide. · Collaborate with product managers, data scientists, and software developers to incorporate models into production processes and influence senior leaders. · Act as ambassador of the fees scientific community.
GB, London
Are you excited about using econometrics and statistics to impact real-world business decisions? We are looking for an Economist to build innovative solutions based on econometrics, experimentation, and machine learning helping us to answer challenging and impactful business problems for Prime Video. If you have an entrepreneurial spirit, you know how to deliver results fast, and you have a deeply quantitative, highly innovative approach to solving problems, and long for the opportunity to build pioneering solutions to challenging problems, we want to talk to you. Key job responsibilities Design, set up, and analyze experiments to measure impact of strategic PV initiatives Leverage causal inference methods to estimate the impact of large-scale investments ex post Collaborate with other scientists at Amazon to deliver measurable progress and change Influence business leaders based on empirical findings A day in the life You will work as an individual contributor working on a mix of complex strategic, long-term research as well as tactical analytical projects. You will write technical as well as business-facing docs and present your work to business leaders and the science community. About the team The Video Economics team consists of PhD-trained structural and causal-inference Economists. We are an high-impact team driving decision-making in areas including PV with ads and content portfolio optimization.