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  • Cross-unit spillovers in A/B testing: Empirical evidence from ads
    Ronak Jain, Stefan Hut, Mahnaz Islam, Yao Pan
    2023 Conference on Digital Experimentation @ MIT (CODE@MIT)
    2023
    Randomized Control Trials (RCTs) are widely used across Amazon to causally estimate impacts of proposed feature changes, in order to make data-driven launch decisions. A key element of experimental design is the level of randomization, and the choice often relies on the cross-unit interaction structure. For instance, in the context of advertiser experiments, a treatment may affect the outcome of control
    Economics
  • Value of stratification in cluster-randomized experiments
    Stefan Hut, Blake Mason, Mahnaz Islam, Lledo Esquerra
    2023 Conference on Digital Experimentation @ MIT (CODE@MIT)
    2023
    There are many experimental settings that may suffer from cross-unit (customers, seller, advertiser, etc.) spillovers, for instance through network effects. Such effects introduce bias and prevent the experimenter from drawing trustworthy insights on the data. One approach to dealing with such spillovers is to group units into clusters and randomize treatment status at the cluster level. Examples of clusters
    Economics
  • The need for a durability index framework for electrical and electronic equipment to support a circular economy
    Hamidreza Habibollahi Najaf Abadi, Maxim Nikiforov, Aaron Krive, Jeffrey W. Herrmann, Mohammad Modarres
    ESREL 2023
    2023
    Enabling a circular economy aims to reduce the amount of global waste generated from electrical and electronic equipment, mitigate the associated risk to the ecosystem and human health, and address concerns over limited material resources. Durability is a critical concern because keeping products in use for a longer time should reduce resource consumption and waste. Assessing the durability of products
    Economics
  • MNL-prophet: Sequential assortment selection under uncertainty
    Vineet Goyal, Salal Humair, Orestis Papadigenopoulos, Assaf Zeevi
    WINE 2023
    2023
    Due to numerous applications in retail and (online) advertising the problem of assortment selection has been widely studied under many combinations of discrete choice models and feasibility constraints. In many situations, however, an assortment of products has to be constructed gradually and without accurate knowledge of all possible alternatives; in such cases, existing offline approaches become inapplicable
    Operations research and optimization
  • OpportunityFinder: A framework for automated causal inference
    Huy Nguyen, Prince Grover, Devashish Khatwani
    KDD 2023 Workshop on Causal Inference and Machine Learning in Practice: Use cases for Product, Brand, Policy and Beyond
    2023
    We introduce OpportunityFinder, a code-less framework for performing a variety of causal inference studies with panel data for non-expert users. In its current state, OpportunityFinder only requires users to provide raw observational data and a configuration file. A pipeline is then triggered that inspects/processes data, chooses the suitable algorithm(s) to execute the causal study. It returns the causal
    Economics
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Economist I, AWS Central Economics and Science
US, CA, San Francisco
We are hiring an 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. 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?
Senior Economist, Survey Methods, People eXperience Technology Central Science (PXTCS)
US, VA, Arlington
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. Ideal candidates will work closely with business partners to develop science that solves the most important business challenges. They will work well in a team setting with individuals from diverse disciplines and backgrounds. They will serve as an ambassador for science and a scientific resource for business teams, so that scientific processes permeate throughout the HR organization to the benefit of Amazonians and Amazon. Ideal candidates will own the development of scientific models and manage the data analysis, modeling, and experimentation that is necessary for estimating and validating models. They will be customer-centric – clearly communicating scientific approaches and findings to business leaders, listening to and incorporate their feedback, and delivering successful scientific solutions. Key job responsibilities Use reduced-form causal analysis and/or structural economic modeling methods to evaluate the impact of change on employee outcomes.
Senior Economist, Fulfillment By Amazon
US, WA, Bellevue
The Fulfillment by Amazon (FBA) and Supply Chain by Amazon (SCA) 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 a Senior Economist with strong causal inference and econometrics skills to join our cross-domain group of economists, applied scientists, research scientists, and data scientists. This person will primarily focus on the new and exciting domain of SCA. As a Senior Economist, you will be part of a high-impact team building cutting edge economic and causal models, developing incentive and recommendation systems, conducting experiments to evaluate and (re)design products and policies, quantifying the impact of FBA and SCA workflows, as well as designing and evaluating economic mechanisms to address the information asymmetries between sellers and Amazon. This person will be collaborating closely with business and software teams to research, innovate, and solve high impact economics problems facing the worldwide FBA business. We are seeking someone who can thrive in a fast-paced, high-energy, and fun-to-work environment, where the team delivers value incrementally and frequently. We value highly technical people who know their subject matter deeply and are willing to learn new areas. We look for individuals who know how to deliver results and show a desire to develop themselves, their colleagues, and their careers. Key job responsibilities - Research and develop causal models at scale to solve diverse and complex economic/business problems faced in FBA/SCA inventory and capacity management systems. - Build economic, statistical, and predictive models to enhance our understanding of seller behavior, preferences, and dynamics. - Provide data-driven guidance on strategic SCA related policy and economic questions. - Propose innovative and rigorous ways of collecting data about our sellers’ expectations and actions to help developing new products and services for our sellers. - Design and conduct randomized experiments to validate theories and improve understanding of Amazon’s third-party seller ecosystem and supply chain. - Develop mechanisms to align millions of sellers’ decisions with those of customers’ needs through better coordinating inventory, inbound, and capacity related decisions. - Collaborate with product managers, scientists, and software developers to incorporate models into production processes and influence senior leaders. About the team Sellers play a vital role in Amazon’s ecosystem, integral to our mission of offering the Earth’s largest selection and lowest prices. FBA is a service that enables third-party sellers to outsource order fulfillment to Amazon, and leverage Amazon’s world-class facilities to provide customers Prime delivery promise. By partnering with Amazon, sellers benefit from powerful, cost-effective solutions that leverage our scale and technology, gain access to Prime members worldwide, increase their sales, and have more time to continue inventing amazing products for customers. With commitment to taking on even more of the supply chain and operational complexities on behalf of our selling partners, Amazon introduced Supply Chain by Amazon (SCA), an end-to-end, fully automated suite of supply chain services. This comprehensive solution empowers sellers to quickly and reliably transport products from manufacturing sites to customers worldwide. Amazon Warehousing and Distribution (AWD) is a pivotal service in SCA that provides best-in-class bulk storage and distribution services to sellers, ensuring they remain well-stocked across all their sale and fulfillment channels while reducing the total supply chain costs. The FBA team is the core group in charge of fulfillment, inventory management, pricing, and a diverse range of operational recommendation services for sellers, as well as building the internal resource management systems. We work to learn seller behavior, understand seller experience, build automated assistants to sellers, recommend right actions to sellers, design seller policies and incentives, and develop science products and services that empower third-party sellers to grow their businesses. To do so, we build and innovate science solutions at the intersection of machine learning, statistics, economics, operations research, and data analytics. We work full-stack, from foundational backend systems to future-forward user interfaces. Our culture is centered on rapid prototyping, rigorous experimentation, and data-driven decision-making.
Economist, Amazon
US, WA, Seattle
The Global Cross-Channel and Cross- Category Marketing (XCM) org are seeking an experienced Economist to join our team. XCM’s mission is to be the most measurably effective and creatively breakthrough marketing organization in the world in order to strengthen the brand, grow the business, and reduce cost for Amazon overall. We achieve this through scaled campaigning in support of brands, categories, and audiences which aim to create the maximum incremental impact for Amazon as a whole by driving the Amazon flywheel. This is a high impact role with the opportunities to lead the development of state-of-the-art, scalable models to measure the efficacy and effectiveness of a new marketing channel. In this critical role, you will leverage your deep expertise in causal inference to design and implement robust measurement frameworks that provide actionable insights to drive strategic business decisions. Key Responsibilities: Develop advanced econometric and statistical models to rigorously evaluate the causal incremental impact of marketing campaigns on customer perception and customer behaviors. Collaborate cross-functionally with marketing, product, data science and engineering teams to define the measurement strategy and ensure alignment on objectives. Leverage large, complex datasets to uncover hidden patterns and trends, extracting meaningful insights that inform marketing optimization and investment decisions. Work with engineers, applied scientists and product managers to automate the model in production environment. Stay up-to-date with the latest research and methodological advancements in causal inference, causal ML and experiment design to continuously enhance the team's capabilities. Effectively communicate analysis findings, recommendations, and their business implications to key stakeholders, including senior leadership. Mentor and guide junior economists, fostering a culture of analytical excellence and innovation.
Senior Economist, Private Brands Discovery
US, WA, Seattle
The Private Brands Discovery team designs innovative machine learning solutions to drive customer awareness for Amazon’s own brands and help customers discover products they love. Private Brands Discovery is an interdisciplinary team of Scientists and Engineers, who incubate and build disruptive solutions using cutting-edge technology to solve some of the toughest science problems at Amazon. To this end, the team employs methods from Natural Language Processing, Deep learning, multi-armed bandits and reinforcement learning, Bayesian Optimization, causal and statistical inference, and econometrics to drive discovery across the customer journey. Our solutions are crucial for the success of Amazon’s own brands and serve as a beacon for discovery solutions across Amazon. This is a high visibility opportunity for someone who wants to have business impact, dive deep into large-scale problems, enable measurable actions on the consumer economy, and work closely with scientists and engineers. As a scientist, you bring business and industry context to science and technology decisions. You set the standard for scientific excellence and make decisions that affect the way we build and integrate algorithms. Your solutions are exemplary in terms of algorithm design, clarity, model structure, efficiency, and extensibility. You tackle intrinsically hard problems, acquiring expertise as needed. You decompose complex problems into straightforward solutions.. With a focus on bias for action, this individual will be able to work equally well with Science, Engineering, Economics and business teams. Key job responsibilities - 5+ yrs of relevant, broad research experience after PhD degree or equivalent. - Advanced expertise and knowledge of applying observational causal interference methods - Strong background in statistics methodology, applications to business problems, and/or big data. - Ability to work in a fast-paced business environment. - Strong research track record. - Effective verbal and written communications skills with both economists and non-economist audiences.
Economist, PXT Central Science
US, VA, Arlington
The People eXperience and Technology Central Science Team (PXTCS) uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, wellbeing, and the value of work to Amazonians. We are an interdisciplinary team that combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal. We are looking for an economist who is able to work with business partners to hone complex problems into specific, scientific questions, and test those questions to generate insights. The ideal candidate will work with engineers and computer scientists 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. We are looking for a creative thinker who can combine a strong technical economic toolbox with a desire to learn from other disciplines, and who knows how to execute and deliver on big ideas as part of an interdisciplinary technical team. Ideal candidates will work closely with business partners to develop science that solves the most important business challenges. They will work in a team setting with individuals from diverse disciplines and backgrounds. They will serve as an ambassador for science and a scientific resource for business teams, so that scientific processes permeate throughout the HR organization to the benefit of Amazonians and Amazon. Ideal candidates will own the data analysis, modeling, and experimentation that is necessary for estimating and validating models. They will work closely with engineering teams to develop scalable data resources to support rapid insights, and take successful models and findings into production as new products and services. They will be customer-centric and will communicate scientific approaches and findings to business leaders, listening to and incorporate their feedback, and delivering successful scientific solutions. Key job responsibilities Use causal inference methods to evaluate the impact of policies on employee outcomes. Examine how external labor market and economic conditions impact Amazon's ability to hire and retain talent. Use scientifically rigorous methods to test solutions for improving employee recognition. A day in the life Work with teammates to apply economic methods to business problems. This might include identifying the appropriate research questions, writing code to implement a DID analysis or estimate a structural model, or writing and presenting a document with findings to business leaders. Our economists also collaborate with partner teams throughout the process, from understanding their challenges, to developing a research agenda that will address those challenges, to help them implement solutions. About the team We are a multidisciplinary team that combines the talents of science and engineering to develop innovative solutions to make Amazon Earth's Best Employer.
Economist , Global Media & Entertainment Economics
US, WA, Seattle
Amazon’s Global Media and Entertainment (GME) organization is creating a future of entertainment where creative content, innovation, and commerce come together. We leverage Amazon’s unique expertise across video, music, gaming, and more to create a truly immersive entertainment experience. Our team, GME Science, is focused on building science tools to optimize Amazon’s entertainment offerings, so that we can provide a great customer experience while operating as a sustainable and profitable business. We push ourselves to Think Big, building ambitious models that create value in multiple GME businesses. This role will expand our team’s measurement work. Business leaders need to quickly understand the long-term impact of various investments, such as new website features, content creation, or marketing campaigns. Our team figures out how to take short-term signals – such as clicks or signups – and turn them into estimates of long-term financial impacts. The right way to design such a metric depends on how the metric will be used, e.g. for a backward-looking evaluation or a forward-looking estimate based on limited realtime signals. We work with measurement teams in each business as well as central teams to build foundational measurement science and adapt it for unique use cases. To be successful in this role, you will need effective communication, an ability to work closely with stakeholders across our many GME partner teams, and the skill to translate data-driven findings into actionable insights. This includes developing a deep understanding of our business context, which is ambiguous and can change quickly. Your work will be used by decision-makers across GME to deliver the best entertainment experience for our customers, which means we have a high bar. Our healthy team culture is supportive and fast-paced, and we prioritize learning, growth, and helping each other to continuously raise the bar. *Impact and Career Growth* In today’s entertainment landscape, critical decisions are made with data and economic models. You’ll help GME leaders ask the right questions, and then deliver data-driven answers, creating the future of GME at Amazon. You’ll help define a long-term science vision in this space and translate it into an actionable roadmap. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding – a perfect recipe for career growth as an economist in tech. Key job responsibilities • Design and build econometric models, especially causal models, to measure the value of the business and its many features • Develop science products from concept to prototype to production, incorporating feedback from scientists and business partners • Independently identify and pursue new opportunities to leverage economic insights across GME businesses • Write business and technical documents communicating business context, methods, and results to business leadership and other scientists • Serve as a technical reviewer for our team and related teams, including document and code reviews
Sr Economist, Amazon Search, Amazon
US, CA, Palo Alto
Amazon is one of the most popular sites in the US. Our product search engine, one of the most heavily used services in the world, indexes billions of products and serves hundreds of millions of customers world-wide. Within Amazon Search, the Data Science team brings expertise in constrained optimization, modeling data, and experimental methods. We partner with internal and external teams to bring create pioneering new insights, develop new measurement and testing methodologies, and hone optimization strategies. We ensure Search's systems, models, and teams properly optimizing over trade-offs in the function of the search experience. In addition to working with partners, we incubate new modeling techniques, and perform 'front line recon' on potential new models and tools. We are looking for a Senior Economist to independently leverage petabyte scale data, seek out opportunities, and deliver models, metrics, or insights that creates wins for customers. Key job responsibilities Measure / Quantify / Expand - Design, size, and analyze field experiments at scale. - Apply econometric or statistical knowledge to improve Amazon Search (using machine learning techniques is a plus) - Retrieve, synthesize, and present critical data in a format that is immediately useful to answering specific questions or improving system performance. - Analyze historical data to identify trends and support decision making. Explore / Enlighten - Formalize assumptions about how Amazon Search is expected to work. - Given anomalies, whether anecdotal or identified automatically, deep dive to explain why they happen, and identify fixes. Decide / Recommend - Build decision-making models and propose solution for the business problem you defined - Analyze A/B tests and extract understanding of customer behavioral responses - Conduct written and verbal presentation to share insights and recommendations to audiences of varying levels of technical sophistication. - Utilize code (python or another object oriented language) for data analyzing and modeling algorithm About the team The mission of the Search Data Science team is to build a world class shopping experience that delights customers. We focus on the long term and big picture, ensuring that the search page is balancing strategic trade-offs. We bring to this effort expertise in constrained optimization, causal inference, and marketplace equilibrium effects. We build systems, metrics, and mechanisms to ensure that product decisions are scientifically sound. We develop models to estimate the downstream dollar value of the quality of the experience. We spend time on evaluating experiments to develop durable learnings.
Intern - Economics (RFCA)
US, WA, Seattle
The Amazon Economics Team is hiring Economist Interns. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets to solve real-world business problems. Some knowledge of econometrics, as well as basic familiarity with Stata, R, or Python is necessary. Experience with SQL, UNIX, Sawtooth, and Spark 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 at Internet speed collaborating with economists, data scientists and MBAʼs. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with future job market placement. Roughly 85% of interns from previous cohorts have converted to full-time economics employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com.
Intern - Economics (FMF)
US, WA, Seattle
The Amazon Economics Team is hiring Economist Interns. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets to solve real-world business problems. Some knowledge of econometrics, as well as basic familiarity with Stata, R, or Python is necessary. Experience with SQL, UNIX, Sawtooth, and Spark 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 at Internet speed collaborating with economists, data scientists and MBAʼs. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with future job market placement. Roughly 85% of interns from previous cohorts have converted to full-time economics employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com.
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