Social

Chirpy Cardinal

This team's goal is to advance the state of open-domain dialogue by blending modern NLP techniques with empathetic, engaging responses.

By Stanford University
5 min read
Share

Stanford's Team Chirpy Cardinal is returning to the competition for the third time.

Our goal is to advance the state of open-domain dialogue by blending modern NLP techniques with empathetic, engaging responses. With each of us bringing a diverse range of backgrounds and experiences to the table, we hope for our bot to be able to provide a great experience for customers, one that makes them feel understood and cared for. We are advised by Prof. Christopher D. Manning.

Chirpy Cardinal (2022)
Team Chirpy Cardinal (2022)

Ryan A. Chi - Team Leader

Chi is a master's student at Stanford working on understanding large language models. Previously, he was the lead contributor for Stanford CRFM's bias investigation team, and was at NVIDIA AI working on NMT systems. This year, he's leading Stanford's Chirpy Cardinal Alexa Prize team. In addition, he has served as the president of Stanford ACM and a coach for the USA Linguistics Olympiad team.

Virginia Adams

Adams is a first year computer science master student, concentrating in AI. She has a background in NLP and worked as an applied research scientist on the conversational AI team at NVIDIA for two years before starting her masters at Stanford.

Thanawan Atchariyachanvanit

Atchariyachanvanit is a sophomore at Stanford University studying computer science. She is an international student from Thailand. Previously, she was an undergraduate research assistant at Stanford NLP Group during the summer.

Steven Cao

Cao is a second-year PhD student at Stanford, researching natural language processing, machine learning, and theory. In the past, he has worked on syntactic parsing, grammar induction, and multilingual modeling. Cao is currently supported by the NSF GRFP.

Gordon Chi

Chi is a master's student at Stanford in building virtual assistants and leveraging large language models. He is a current mentor of the Stanford Undergraduate Research in CS program, and was a former President of Stanford ACM and International Linguistics Olympiad Contestant. He is passionate about applying game-playing learning approaches in real world domains in NLP and healthcare.

Nathan A. Chi

Chi is a sophomore at Stanford, studying computer science. He is interested in AI and its applications to healthcare. In addition to the Stanford AI Lab, he is a section leader for Stanford’s introductory CS course and serves as the current president of Stanford ACM. He researches chatbots and biomedical AI at Stanford, Yale and Harvard.

Gary Dai

Dai is an Aeronautical and Astronautical Engineering masters student concentrating in ML, robotics, and control. He worked at Meta for internship on Recommendation system and Language model knowledge distillations.

Scott Hickmann

Scott is a sophomore studying Computer Science at Stanford University. He is a full-stack developer who worked on computer vision and NLP projects in industry. Scott is also teaching a web development class for Stanford ACM. He is looking forward to delving deeper into scalable NLP solutions to improve real-world customer-facing interactions.

Jeremy Kim - Co-Team Leader

Kim is a junior at Stanford University studying mathematics, originally from New York City. Kim loves brainstorming solutions to problems with other people, and is eager to pursue his passions through the Alexa Prize contest.

Siyan Li

Li is a second-year master's student in computer science at Stanford University. Her current research focus is on dialogue systems, both in speech and text. She has been an intern at Amazon Robotics, and conducted research at Carnegie Mellon, University of Southern California, and IT University of Copenhagen. Li will be applying to PhD programs this year, and she is passionate about applying dialogue systems in healthcare contexts, specifically in a mental health related context.

Kristie Park

Park is a junior at Stanford double majoring in Biomechanical Engineering and Sociology with a minor in Music. She is interested in the intersection between graphic design and empathetic communication.

Shashank Rammoorthy

Rammoorthy is a masters student in Computer Science, interested in systems and ML, and is especially curious about what makes systems scale well. Previously, he did self supervised learning research at the Stanford CoCo Lab.

Parth Sarthi

Sarthi is a sophomore at Stanford studying Computer Science. He has an interest in Retrieval Augmented Generation and reducing hallucinations in Large Language Models. Additionally, Sarthi is a part of the Contextual AI research team.

Ji Hun Wang

Wang is a junior at Stanford, majoring in Computer Science and Linguistics. His research explores the explainability of Large Language Models, multimodal deep learning, and the use of deep learning to study formal aspects of natural languages. He is currently a member of the Stanford ML Group.

Brian Y. Xu

Xu is an undergraduate studying mathematics and statistics who is interested in analyzing human behavior and answering social questions through the lens of robust mathematical modeling. He previously interned with the CS Theory Group.

Brian Z. Xu

Xu is a sophomore at Stanford studying Mathematical and Computational Science. He is also a research engineer with the Stanford Social Neuroscience Lab. Xu is interested in exploring creative uses of natural language processing for problem-solving.

Katherine Yu

Yu is a junior at Stanford University studying computer science and mathematics. She has done research with the Stanford Theory Group on sampling algorithms, and is interested in computer systems and user- facing software.

Christopher Manning - Faculty advisor

Manning is the inaugural Thomas M. Siebel Professor in Machine Learning in the Departments of Linguistics and Computer Science at Stanford University, Director of the Stanford Artificial Intelligence Laboratory (SAIL), and an Associate Director of the Stanford Human-Centered Artificial Intelligence Institute (HAI). His research goal is computers that can intelligently process, understand, and generate human language material. Manning is a leader in applying Deep Learning to Natural Language Processing, with well-known research on the GloVe model of word vectors, question answering, tree-recursive neural networks, machine reasoning, neural network dependency parsing, neural machine translation, sentiment analysis, and deep language understanding.

About the Author
Stanford University

Latest news

The latest updates, stories, and more about Alexa Prize.
Load more

Work with us

See more jobs See more jobs
Applied Scientist, AGI
US, CA, Sunnyvale
The Artificial General Intelligence (AGI) team is looking for a highly skilled and experienced Applied Scientist, to support the development and implementation of state-of-the-art algorithms and models for supervised fine-tuning and reinforcement learning through human feedback and and complex reasoning; with a focus across text, image, and video modalities. As an Applied Scientist, you will play a critical role in supporting the development of Generative AI (Gen AI) technologies that can handle Amazon-scale use cases and have a significant impact on our customers' experiences. Key job responsibilities - Collaborate with cross-functional teams of engineers, product managers, and scientists to identify and solve complex problems in Gen AI - Design and execute experiments to evaluate the performance of different algorithms and models, and iterate quickly to improve results - Think big about the arc of development of Gen AI over a multi-year horizon, and identify new opportunities to apply these technologies to solve real-world problems - Communicate results and insights to both technical and non-technical audiences, including through presentations and written reports
Applied Scientist, AWS Neuron Science Team
US, CA, Santa Clara
The AWS Neuron Science Team is looking for talented scientists to enhance our software stack, accelerating customer adoption of Trainium and Inferentia accelerators. In this role, you will work directly with external and internal customers to identify key adoption barriers and optimization opportunities. You'll collaborate closely with our engineering teams to implement innovative solutions and engage with academic and research communities to advance state-of-the-art ML systems. As part of a strategic growth area for AWS, you'll work alongside distinguished engineers and scientists in an exciting and impactful environment. We actively work on these areas: - AI for Systems: Developing and applying ML/RL approaches for kernel/code generation and optimization - Machine Learning Compiler: Creating advanced compiler techniques for ML workloads - System Robustness: Building tools for accuracy and reliability validation - Efficient Kernel Development: Designing high-performance kernels optimized for our ML accelerator architectures A day in the life AWS Utility Computing (UC) provides product innovations that continue to set AWS’s services and features apart in the industry. As a member of the UC organization, you’ll support the development and management of Compute, Database, Storage, Platform, and Productivity Apps services in AWS, including support for customers who require specialized security solutions for their cloud services. Additionally, this role may involve exposure to and experience with Amazon's growing suite of generative AI services and other cloud computing offerings across the AWS portfolio. About the team AWS Neuron is the software of Trainium and Inferentia, the AWS Machine Learning chips. Inferentia delivers best-in-class ML inference performance at the lowest cost in the cloud to our AWS customers. Trainium is designed to deliver the best-in-class ML training performance at the lowest training cost in the cloud, and it’s all being enabled by AWS Neuron. Neuron is a Software that include ML compiler and native integration into popular ML frameworks. Our products are being used at scale with external customers like Anthropic and Databricks as well as internal customers like Alexa, Amazon Bedrocks, Amazon Robotics, Amazon Ads, Amazon Rekognition and many more. About the team Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the preferred 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 AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do. 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.
Sr. Applied Scientist, Amazon Ads
US, WA, Seattle
Application deadline: Applications will be accepted on an ongoing basis Amazon Ads is re-imagining advertising through cutting-edge generative artificial intelligence (AI) technologies. We combine human creativity with AI to transform every aspect of the advertising life cycle—from ad creation and optimization to performance analysis and customer insights. Our solutions help advertisers grow their brands while enabling millions of customers to discover and purchase products through delightful experiences. We deliver billions of ad impressions and millions of clicks daily, breaking fresh ground in product and technical innovations. 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. As a Senior Applied Scientist at Amazon Ads, you will: • Research and implement cutting-edge machine learning (ML) approaches, including applications of generative AI and large language models • Develop and deploy innovative ML solutions spanning multiple disciplines, from ranking and personalization to natural language processing, computer vision, recommender systems, and large language models • Drive end-to-end projects that tackle ambiguous problems at massive scale, often working with petabytes of data • Build and optimize models that balance multiple stakeholder needs, helping customers discover relevant products while enabling advertisers to achieve their goals efficiently • Build ML models, perform proof-of-concept, experiment, optimize, and deploy your models into production, working closely with cross-functional teams that include engineers, product managers, and other scientists • Design and run A/B experiments to validate hypotheses, gather insights from large-scale data analysis, and measure business impact • Develop scalable, efficient processes for model development, validation, and deployment that optimize traffic monetization while maintaining customer experience Why you’ll love this role: This role offers unprecedented breadth in ML applications and access to extensive computational resources and rich datasets that will enable you to build truly innovative solutions. You'll work on projects that span the full advertising life cycle, from sophisticated ranking algorithms and real-time bidding systems to creative optimization and measurement solutions. You'll work alongside talented engineers, scientists, and product leaders in a culture that encourages innovation, experimentation, and bias for action, and you’ll directly influence business strategy through your scientific expertise. What makes this role unique is the combination of scientific rigor with real-world impact. You’ll re-imagine advertising through the lens of advanced ML while solving problems that balance the needs of advertisers, customers, and Amazon's business objectives. Your impact and career growth: Amazon Ads is investing heavily in AI and ML capabilities, creating opportunities for scientists to innovate and make their marks. Your work will directly impact millions. Whether you see yourself growing as an individual contributor or moving into people management, there are clear paths for career progression. This role combines scientific leadership, organizational ability, technical strength, and business understanding. You'll have opportunities to lead technical initiatives, mentor other scientists, and collaborate with senior leadership to shape the future of advertising technology. Most importantly, you'll be part of a community that values scientific excellence and encourages you to push the boundaries of what's possible with AI. Watch two Applied Scientists at Amazon Ads talk about their work: https://www.youtube.com/watch?v=vvHsURsIPEA Learn more about Amazon Ads: https://advertising.amazon.com/ Key job responsibilities As an Applied Scientist in Amazon Ads, you will: - Research and implement cutting-edge ML approaches, including applications of generative AI and large language models - Develop and deploy innovative ML solutions spanning multiple disciplines – from ranking and personalization to natural language processing, computer vision, recommender systems, and large language models - Drive end-to-end projects that tackle ambiguous problems at massive scale, often working with petabytes of data - Build and optimize models that balance multiple stakeholder needs - helping customers discover relevant products while enabling advertisers to achieve their goals efficiently - Build ML models, perform proof-of-concept, experiment, optimize, and deploy your models into production, working closely with cross-functional teams including engineers, product managers, and other scientists - Design and run A/B experiments to validate hypotheses, gather insights from large-scale data analysis, and measure business impact - Develop scalable, efficient processes for model development, validation, and deployment that optimize traffic monetization while maintaining customer experience A day in the life Why you will love this role: This role offers unprecedented breadth in ML applications, and access to extensive computational resources and rich datasets that enable you to build truly innovative solutions. You'll work on projects that span the full advertising lifecycle - from sophisticated ranking algorithms and real-time bidding systems to creative optimization and measurement solutions. You'll also work alongside talented engineers, scientists and product leaders in a culture that encourages innovation, experimentation, and bias for action where you’ll directly influence business strategy through your scientific expertise. What makes this role unique is the combination of scientific rigor with real-world impact. You’ll re-imagine advertising through the lens of advanced ML while solving problems that balance the needs of advertisers, customers, and Amazon's business objectives. About the team Your impact and career growth: Amazon Ads is investing heavily in AI and ML capabilities, creating opportunities for scientists to innovate and make their mark. Your work will directly impact millions. Whether you see yourself growing as an individual contributor or moving into people management, there are clear paths for career progression. This role combines scientific leadership, organizational ability, technical strength, and business understanding. You'll have opportunities to lead technical initiatives, mentor other scientists, and collaborate with senior leadership to shape the future of advertising technology. Most importantly, you'll be part of a community that values scientific excellence and encourages you to push the boundaries of what's possible with AI. Watch two applied scientists at Amazon Ads talk about their work: https://www.youtube.com/watch?v=vvHsURsIPEA Learn more about Amazon Ads: https://advertising.amazon.com/
Applied Scientist II, Sponsored Products Autonomous Campaigns
US, NY, New York
We are looking for a passionate Applied Scientist to help pioneer the next generation of agentic AI applications for Amazon advertisers. In this role, you will design agentic architectures, develop tools and datasets, and contribute to building systems that can reason, plan, and act autonomously across complex advertiser workflows. You will work at the forefront of applied AI, developing methods for fine-tuning, reinforcement learning, and preference optimization, while helping create evaluation frameworks that ensure safety, reliability, and trust at scale. You will work backwards from the needs of advertisers—delivering customer-facing products that directly help them create, optimize, and grow their campaigns. Beyond building models, you will advance the agent ecosystem by experimenting with and applying core primitives such as tool orchestration, multi-step reasoning, and adaptive preference-driven behavior. This role requires working independently on ambiguous technical problems, collaborating closely with scientists, engineers, and product managers to bring innovative solutions into production. Key job responsibilities - Design and build agents for our autonomous campaigns experience. - Design and implement advanced model and agent optimization techniques, including supervised fine-tuning, instruction tuning and preference optimization (e.g., DPO/IPO). - Curate datasets and tools for MCP. - Build evaluation pipelines for agent workflows, including automated benchmarks, multi-step reasoning tests, and safety guardrails. - Develop agentic architectures (e.g., CoT, ToT, ReAct) that integrate planning, tool use, and long-horizon reasoning. - Prototype and iterate on multi-agent orchestration frameworks and workflows. - Collaborate with peers across engineering and product to bring scientific innovations into production. - Stay current with the latest research in LLMs, RL, and agent-based AI, and translate findings into practical applications. About the team The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through the latest 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. The Autonomous Campaigns team within Sponsored Products and Brands is focused on guiding and supporting 1.6MM advertisers to meet their advertising needs of creating and managing ad campaigns. At this scale, the complexity of diverse advertiser goals, campaign types, and market dynamics creates both a massive technical challenge and a transformative opportunity: even small improvements in guidance systems can have outsized impact on advertiser success and Amazon’s retail ecosystem. Our vision is to build a highly personalized, context-aware campaign creation and management system that leverages LLMs together with tools such as auction simulations, ML models, and optimization algorithms. This agentic framework, will operate across both chat and non-chat experiences in the ad console, scaling to natural language queries as well as proactively delivering guidance based on deep understanding of the advertiser. To execute this vision, we collaborate closely with stakeholders across Ad Console, Sales, and Marketing to identify opportunities—from high-level product guidance down to granular keyword recommendations—and deliver them through a tailored, personalized experience. Our work is grounded in state-of-the-art agent architectures, tool integration, reasoning frameworks, and model customization approaches (including tuning, MCP, and preference optimization), ensuring our systems are both scalable and adaptive.
Applied Scientist, Artificial General Intelligence
US, CA, Sunnyvale
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 generative AI (GenAI) and multi-modal systems. Key job responsibilities As an Applied Scientist with the AGI team, you will work with talented peers to develop algorithms and modeling techniques to advance the state of the art with multi-modal 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 large-scale computing resources to accelerate development with multi-modal Large Language Models (LLMs) and 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.
Member of Technical Staff - Machine Learning Engineer, AGI Autonomy
US, CA, San Francisco
The AGI Autonomy Perception team performs applied machine learning research, including model training, dataset design, pre- and post- training. We train Nova Act, our state-of-the art computer use agent, to understand arbitrary human interfaces in the digital world. We are seeking a Machine Learning Engineer who combines strong ML expertise with software engineering excellence to scale and optimize our ML workflows. You will be a key member on our research team, helping accelerate the development of our leading computer-use agent. We are seeking a strong engineer who has a passion for scaling ML models and datasets, designing new ML frameworks, improving engineering practices, and accelerating the velocity of AI development. You will be hired as a Member of Technical Staff. Key job responsibilities * Design, build, and deploy machine learning models, frameworks, and data pipelines * Optimize ML training, inference, and evaluation workflows for reliability and performance * Evaluate and improve ML model performance and metrics * Develop tools and infrastructure to enhance ML development productivity
Senior Applied Scientist, Sponsored Products and Brands
US, WA, Seattle
The Sponsored Products and Brands (SPB) 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. This position will be part of the Conversational Ad Experiences team within the Amazon Advertising organization. Our cross-functional team focuses on designing, developing and launching innovative ad experiences delivered to shoppers in conversational contexts. We utilize leading-edge engineering and science technologies in generative AI to help shoppers discover new products and brands through intuitive, conversational, multi-turn interfaces. We also empower advertisers to reach shoppers, using their own voice to explain and demonstrate how their products meet shoppers' needs. We collaborate with various teams across multiple Amazon organizations to push the boundary of what's possible in these fields. We are seeking a science leader for our team within the Sponsored Products & Brands organization. You'll be working with talented scientists, engineers, and product managers to innovate on behalf of our customers. An ideal candidate is able to navigate through ambiguous requirements, working with various partner teams, and has experience in generative AI, large language models (LLMs), information retrieval, and ads recommendation systems. Using a combination of generative AI and online experimentation, our scientists develop insights and optimizations that enable the monetization of Amazon properties while enhancing the experience of hundreds of millions of Amazon shoppers worldwide. If you're fired up about being part of a dynamic, driven team, then this is your moment to join us on this exciting journey! Key job responsibilities - Serve as a tech lead for defining the science roadmap for multiple projects in the conversational ad experiences space powered by LLMs. - Build POCs, optimize and deploy models into production, run experiments, perform deep dives on experiment data to gather actionable learnings and communicate them to senior leadership - Work closely with software engineers on detailed requirements, technical designs and implementation of end-to-end solutions in production. - Work closely with product managers to contribute to our mission, and proactively identify opportunities where science can help improve customer experience - Research new machine learning approaches to drive continued scientific innovation - Be a member of the Amazon-wide machine learning community, participating in internal and external meetups, hackathons and conferences - Help attract and recruit technical talent, mentor scientists and engineers in the team
Intern - Economics (STRUC)
US, WA, Seattle
Amazon Economics is seeking Structural Economist (STRUC) Interns who are passionate about applying structural econometric methods to solve real-world business challenges. STRUC economists specialize in the econometric analysis of models that involve the estimation of fundamental preferences and strategic effects. In this full-time internship (40 hours per week, with hourly compensation), you'll work with large-scale datasets to model strategic decision-making and inform business optimization, gaining hands-on experience that's directly applicable to dissertation writing and future career placement. Key job responsibilities As a STRUC Economist Intern, you'll specialize in structural econometric analysis to estimate fundamental preferences and strategic effects in complex business environments. Your responsibilities include: - Analyze large-scale datasets using structural econometric techniques to solve complex business challenges - Applying discrete choice models and methods, including logistic regression family models (such as BLP, nested logit) and models with alternative distributional assumptions - Utilizing advanced structural methods including dynamic models of customer or firm decisions over time, applied game theory (entry and exit of firms), auction models, and labor market models - Building datasets and performing data analysis at scale - Collaborating with economists, scientists, and business leaders to develop data-driven insights and strategic recommendations - Tackling diverse challenges including pricing analysis, competition modeling, strategic behavior estimation, contract design, and marketing strategy optimization - Helping business partners formalize and estimate business objectives to drive optimal decision-making and customer value - Build and refine comprehensive datasets for in-depth structural economic analysis - Present complex analytical findings to business leaders and stakeholders
Intern - Economics (RFCA)
US, WA, Seattle
Amazon Economics is seeking Reduced Form Causal Analysis (RFCA) Economist Interns who are passionate about applying econometric methods to solve real-world business challenges. RFCA represents the largest group of economists at Amazon, and these core econometric methods are fundamental to economic analysis across the company. In this full-time internship (40 hours per week, with hourly compensation), you'll work with large-scale datasets to analyze causal relationships and inform strategic business decisions, gaining hands-on experience that's directly applicable to dissertation writing and future career placement. Key job responsibilities As an RFCA Economist Intern, you'll specialize in econometric analysis to determine causal relationships in complex business environments. Your responsibilities include: - Analyze large-scale datasets using advanced econometric techniques to solve complex business challenges - Applying econometric techniques such as regression analysis, binary variable models, cross-section and panel data analysis, instrumental variables, and treatment effects estimation - Utilizing advanced methods including differences-in-differences, propensity score matching, synthetic controls, and experimental design - Building datasets and performing data analysis at scale - Collaborating with economists, scientists, and business leaders to develop data-driven insights and strategic recommendations - Tackling diverse challenges including program evaluation, elasticity estimation, customer behavior analysis, and predictive modeling that accounts for seasonality and time trends - Build and refine comprehensive datasets for in-depth economic analysis - Present complex analytical findings to business leaders and stakeholders
Intern - Economics (FMF)
US, WA, Seattle
Amazon Economics is seeking Forecasting, Macroeconomics and Finance (FMF) Economist Interns who are passionate about applying time-series econometric methods to solve real-world business challenges. FMF economists interpret and forecast Amazon business dynamics by combining advanced time-series statistical methods with strong economic analysis and intuition. In this full-time internship (40 hours per week, with hourly compensation), you'll work with large-scale datasets to forecast business trends and inform strategic decisions, gaining hands-on experience that's directly applicable to dissertation writing and future career placement. Key job responsibilities As an FMF Economist Intern, you'll specialize in time-series econometric analysis to understand, predict, and optimize Amazon's business dynamics. Your responsibilities include: - Analyze large-scale datasets using advanced time-series econometric techniques to solve complex business challenges - Applying frontier methods in time series econometrics, including forecasting models, dynamic systems analysis, and econometric models that combine macro and micro data - Developing formal models to understand past and present business dynamics, predict future trends, and identify relevant risks and opportunities - Building datasets and performing data analysis at scale using world-class data tools - Collaborating with economists, scientists, and business leaders to develop data-driven insights and strategic recommendations - Tackling diverse challenges including analyzing drivers of growth and profitability, forecasting business metrics, understanding how customer experience interacts with external conditions, and evaluating short, medium, and long-term business dynamics - Build and refine comprehensive datasets for in-depth time-series economic analysis - Present complex analytical findings to business leaders and stakeholders
See more jobs
View from space of a connected network around planet Earth representing the Internet of Things.
Get more from Amazon Science
Subscribe to our monthly newsletter

Amazon.com | Conditions of Use | Privacy | © 1996-2025 Amazon.com, Inc. or its affiliates

Social