Howard University's Founders Library is seen in the distance.
Howard University's Founders Library is seen in the distance. Howard is hosting AEASP “in support of increasing the pipeline of underrepresented minority economists.”
Oscar Merrida IV

Amazon to sponsor Howard University summer program aimed at increasing pipeline of minority economists

Howard University is the first Black college to host the American Economic Association Summer Training and Scholarship Program.

Howard University recently announced that it will host the American Economic Association Summer Training and Scholarship Program (AEASP) “in support of increasing the pipeline of underrepresented minority economists.” The program will be hosted at Howard for the next five years, and Amazon is sponsoring next summer’s program. Amazon first began discussions with Howard University about sponsoring AEASP about two years ago. The program, which aims to prepare “talented undergraduates for doctoral programs in economics and related disciplines,” will celebrate its 50th anniversary in 2024 at Howard.

"The lack of diversity in economics becomes self-reinforcing"

Four economists from diverse backgrounds shared how economics can address its diversity problem and talked about how their lives have shaped their work as economists.

That Howard, an historically Black college and university (HBCU) which produces more Black economics undergrads than any other institution, is hosting AEASP for the first time serves as a reminder of the progress the economics profession still must make.

The Caucus of Black Economists (later called the National Economics Association) first began exploring the issues of underrepresentation of minorities within the economics field in 1969. More than 50 years later, the economics profession is still grappling with structural issues. In fact, last January’s AEA conference in San Diego featured a panel titled, “How Can Economics Solve Its Race Problem.”

Rhonda Vonshay Sharpe and Omari H. Swinton standing in front of Howard University.
Rhonda Vonshay Sharpe, left, and Omari H. Swinton, right, are seen posing on the campus of Howard University. They discussed why economics still struggles with diversity.
Oscar Merrida IV

Omari H. Swinton, the chair of Howard University’s Department of Economics, who is both an alumni and the current director of the AEA summer program, as well as the past president of the National Economics Association, has observed that, “The vast majority of institutions in the US have never had a Black economist on staff, and the vast majority of schools have never graduated a Black PhD economist.”

Rhonda Vonshay Sharpe, the founder and president of the Women's Institute for Science, Equity, and Race (WISER), which is also a partner in next summer’s AEASP program, authored a research paper in 2019 that found that from 1966 to 2015, “the number of undergraduate economics degrees conferred to Black women was stagnant, and there was a decrease in the number of doctorates conferred to Black men.”

So why does the economics field still have such a massive disparity in minority representation? What needs to happen for systemic progress to be made? Amazon Science sat down with Sharpe and Swinton to ask those questions, as well as why Howard hosting the summer program is so significant, and what advice they would give to students considering economics as a major or profession. We also talked with Amazon chief economist Pat Bajari to find out why Amazon is sponsoring next summer’s AEASP program, and why he thinks diversity within the economics profession is essential.

A Howard University sign on the Howard campus
The AEASP will celebrate its 50th anniversary in 2024 at Howard University.
Oscar Merrida IV

Why does economics still have such a significant diversity problem?

Omari H. Swinton: I don't know that economics, as a profession, has really agreed that there's a problem. I think that's one of the big issues—we’ll say there's a problem, but nothing ever changes. You oftentimes hear people say things like, ‘We want to increase diversity’ but don't actually make any changes. They just say that that's something that they want to do.

It’s not as if these things haven't been out there. There are people out there who have dedicated their lives to bringing these types of issues to the forefront. I go back to Sandy Darity as an example. If you read from his earlier work, he's talking about these issues. Gregory Price has chronicled which institutions have Black economists in them. Rhonda has been looking at these issues for years.

Whether the economics profession is really ready to change is the issue. There have been a lot of people who have been talking about these issues for years. Others have come out and mentioned these problems more recently, but they ignore the fact that people have been talking about issues of underrepresentation for years.

Rhonda Vonshay Sharpe on the campus of Howard University
Rhonda Vonshay Sharpe says economics needs to define what diversity means. "If you don't define it, you can't measure it, or hold folks accountable."
Oscar Merrida IV

Rhonda Vonshay Sharpe: I narrow the problem down to be three things: 1) Economics has never defined what diversity means, and if you don't define it, you can't measure it, or hold folks accountable; 2) We don't have accurate data to track progress. We need to collect data that can be disaggregated by characteristics that have been used to limit participation in the profession. For example, when you talk about women, that usually means white women, and when we talk in terms of race, then you're really talking about men, and both of those descriptors are biased; and 3) As Omari said, there's enormous erasure happening. People have been doing this a long time, yet newcomers who have recently gotten tenure suddenly feel bad. They are handed a mic as if they are now the authorities. They don’t bother to understand whose shoulders they're standing on.

What needs to happen to address this problem? What role can academic institutions and companies like Amazon play?

Sharpe: I don't think the answer is to hire more Black economists. I really don't. And here is why: Because I think that when people say, ‘hire more Black economists’, people do just that, they hire Black economists. They do not think about whether or not those Black economists are bringing lived experiences that are going to help you craft policies to better interact with your customers.

One of the things I've been saying to folks recently is we need to talk more about structural classism and the ways in which we treat folks who are poor. So, it's not just about hiring Black economists, it's not about hiring Hispanic economists. It's about hiring folks who have lived experience in the US that will get at the inequality and related issues. That's not going to be solved just by hiring an economist because they are non-white.

Omari H. Swinton, the chair of Howard University's Department of Economics, on Howard's campus.
Omari H. Swinton says the AEASP program coming to Howard "is important because this is what our program is designed to do: increase minority participation in the economics profession."
Oscar Merrida IV

Swinton: If you say you want to diversify the profession, then stop looking at things that are not really problems. For example, there's not really a pipeline problem. You can ask almost any economics professor who teaches Principles of Economics, and most will tell you that is probably one of the worst classes to use if you want somebody to be interested in economics as a profession. But it really hasn't changed in years.

One change that we're making in the summer program is the experiential internship, or experiential learning. We’re going to place students with think tanks and corporations to actually see what an economist outside of the academy does. Everybody that gets a PhD in economics isn't going to be able to get a job as a professor. What does it look like to be an economist at Amazon? What does it look like to be an economist at the Census Bureau or at Brookings? Those are entirely different experiences. We’re trying to partner with as many different organizations as possible.

Hopefully we'll see change at those institutions, because students will come to the summer program, have that experience, and want to go back to those institutions. Rather than wanting to be a professor, they will, for example, say, ‘I want to be an economist at the Census Bureau, because I believe this research is important.’ It’s important for organizations, public and private, to be available to students, so they can see the type of experiences they can have if they work for you.

Pat Bajari
Pat Bajari, Amazon vice president and chief economist
Carl Clark, Amazon Imaging Studio

Pat Bajari: As an economist, I have always thought of this is in terms of diminishing returns. If you always have the same type of viewpoint, and keep hiring replicas of that viewpoint, the returns you get from that eventually decrease. Having different viewpoints allows you to do better work. And because we serve a large and diverse base of customers, we have a large and diverse base of problems. We want to take a leading role in supporting a new generation of economists from underrepresented minorities—it is not only the right thing to do, but it will also help bring strong and diverse voices that will create an even more inclusive customer experience.

When individuals come from different backgrounds, they bring different perspectives to the table. You do better work when you have different perspectives.
Pat Bajari

Swinton: One thing organizations can do is find programs that are actually successful at achieving the types of goals they’re pursuing. For example, some of the research done by Becker et al. shows that about 20 percent of Blacks that have PhDs in economics have attended the AEASP program. By helping support Howard in hosting AEASP in this first year, Amazon is doing that. Without Amazon’s support, Howard wouldn't be able to host the AEA summer program at all. We certainly hope others will follow Amazon’s lead.

What is the significance of the summer program coming to Howard?

Swinton: The summer program is extremely important in my path as an economist. My first cohort of economists were the people that I met through the summer program. Howard is the number one producer as an undergraduate feeder of Blacks who go on to get PhDs in economics. This is our mission and one of our goals as an institution and as a department, and I think the AEA summer program coming to Howard is important because this is what our program is designed to do: increase minority participation in the economics profession.

The National Economics Association summer program came out of Marcus Alexis’ mind as a program to help get minorities interested in economics. For the AEASP program to come to Howard at this point in time is a great honor. It’s an honor to be the first HBCU to host the summer program.

Sharpe: I'm excited to see a program that's going to be led by Blacks, which I think is incredibly important, as the program will celebrate 50 years while it's at Howard in 2024. It just feels full circle in terms of thinking about Marcus Alexis, who was a Black economist, and then having the program 50 years later be at an institution that is the number one producer of Black economists. That's incredibly exciting.

Finally, what advice would you give to someone considering whether to pursue a degree in economics? Why is economics such an important field?

Bajari: A lot of economics is understanding people's material wellbeing. Who has low wages? Who has high wages? If you take a given policy, whether that's central bank policy or interventions into labor markets, etcetera, these things deeply, deeply, deeply affect people's lives, people's material outcomes. What they can purchase and where they can live and where they can send their kids to school and so forth. It's an important set of questions, and they range from micro things about what happens to the individual, to macro things, such as how the whole world is evolving and changing in response to things like COVID-19.

Howard University's Founders Library
Howard University's Founders Library is seen here. Howard is the first Black college to host AEASP.
Oscar Merrida IV

If we change policy or somebody goes to college versus doesn't go to college, what are the implications of those economic variables? I know this is what attracted me to economics. As a young person, growing up pretty poor in rural Minnesota, I was interested in the world and how it worked. And I liked economics because it brought math and data and scientific formalism to those questions. That's not the only way you can look at those questions, or the only way you should look at them, but it’s one way that's highly useful.

Sharpe: For students pursuing a PhD in economics, my main advice is to pick a PhD program that's a good fit for you. Many students think that if you don't go to a top program, you can't have a successful career. That’s not true. I went to Claremont Graduate University, not highly ranked, but I had an amazing time as a graduate student. I loved it. My mentee when I was in graduate school was Olugbenga Ajilore who’s at CAP (Center for American Progress) now, who is a rock star right now in terms of being in the news and asking people to think about rural communities. He and I didn't go to top economics departments, but we went to places that were good fits for us, and that's incredibly important.

Bajari: “Technology economics” is a booming field. The largest conference held by the National Association of Business Economists is now the tech economics conference. It’s larger than their annual conference now, because it's been an explosive area of job growth for young people. We are one of the larger private sector employers of economists. When you're in that role, you have an obligation to demonstrate leadership. We saw sponsorship of AEASP as an opportunity to expose young PhDs to this emerging field. I thought Howard was very thoughtful about their proposal, and I'm hoping AEASP can help us establish a pipeline of highly qualified candidates.

Swinton: I talk to students about this all the time. You want to make a change, and you want to be a policy maker? Be an economist. You want to go into business and work on Wall Street, make a lot of money? Be an economist. Economics is one of the most useful majors because it allows you the opportunity you to go out and do a wide variety of things based on the basic training you obtain.

Applications for the summer program are open and the deadline to apply is January 31, 2021. To apply, visit economics.howard.edu/aeasp. The program will be held May 27 to July 25, 2021, and be offered in Washington, D.C., contingent upon COVID-19 restrictions.

Research areas

Related content

IN, KA, Bengaluru
Amazon Ads is a multi-billion dollar global business that delivers advertising experiences across Amazon's owned-and-operated properties (including Prime Video, Twitch, Fire TV, and Amazon.com), third-party publisher networks, and emerging channels like generative AI-powered shopping experiences. As one of the fastest-growing segments of Amazon, we operate at unprecedented scale across desktop, mobile, connected TV, and emerging surfaces. Within Amazon Ads, Traffic Quality is a critical pillar of advertiser trust and marketplace integrity. Our mission is to build advanced capabilities that work at petabyte scale to detect sophisticated invalid traffic (IVT) which includes sophisticated non-human traffic, bot networks, and fraudulent engagement patterns across programmatic advertising. We are on a journey to establish Amazon Ads as an industry leader in traffic quality standards and transparency. Our research agenda focuses on staying ahead of adversarial actors through continuous innovation in detection methodologies, leveraging state-of-the-art techniques in deep learning and generative modeling, user behavior and multi-modal representation learning, anomaly detection, time-series analysis, and sparse labeling methods. We process billions of ad events daily, developing novel algorithms that balance precision and recall while operating under strict latency constraints. Our work directly protects hundreds of millions of dollars in advertiser spend annually while maintaining a seamless user experience. Key job responsibilities As a Data Scientist II in Traffic Quality, you will solve inherently hard problems in advertising fraud detection by applying advanced statistical techniques and machine learning. You'll work on systems that process billions of ad impressions and clicks per day, using Amazon's cloud services including EC2, S3, EMR, Sagemaker, and RedShift. - Define and frame new research problems in fraud detection where neither problem nor solution is well-defined. - Apply new machine learning approaches, models, and algorithms to detect sophisticated invalid traffic. - Apply domain knowledge to perform broad data analysis as a precursor to modeling and build business insights. - Work with unstructured and massive datasets to deliver results. - Produce research reports meeting top-tier external publication standards. - Mentor and develop junior scientists on the team. About the team Here are a few papers published by the team: 1/ [Scaling Generative Pre-training for User Ad Activity Sequences. AdKDD 2023.](https://assets.amazon.science/b7/42/03be071743d5a57cb1656e6caa34/scaling-generative-pre-training-for-user-ad-activity-sequences.pdf) 2/ [SLIDR: Real-time Robot Detection On Online Ads, IAAI 2023, Deployed Highly Innovative Applications of AI Track (AAAI 2023)](https://assets.amazon.science/75/2f/3b7106b143f38f7f4d2806388ace/real-time-detection-of-robotic-traffic-in-online-advertising.pdf) 3/ [Self-supervised Representation Learning Across Sequential and Tabular Features Using Transformers, NeurIPS 2022, First Table Representation Learning Workshop](https://openreview.net/forum?id=wIIJlmr1Dsk)
US, CA, Sunnyvale
We are seeking an Applied Scientist to focus on Robot Navigation. In this role, you'll research and develop advanced navigation systems that enable robots to move reliably and safely through complex, dynamic environments. You'll work across a broad spectrum of navigation approaches—from classical methods to learning-based techniques and foundation models—to build robust solutions for autonomous robot navigation. Key job responsibilities - Develop and implement robust navigation systems that enable reliable autonomous operation in complex, dynamic indoor environments with static and dynamic obstacles - Build simulation-based and on-device evaluation frameworks with comprehensive benchmarks and metrics for systematic comparison of navigation methods - Conduct sim-to-real transfer experiments, analyzing performance gaps and developing techniques to ensure reliable real-world navigation performance - Collaborate with world model, manipulation, and other teams to ensure seamless integration of navigation capabilities into the full robot system - Stay current with the latest advances in robot navigation, spatial reasoning, and related fields, and apply relevant findings to improve system performance - Mentor fellow scientists and engineers while maintaining strong individual technical contributions About the team Fauna Robotics, an Amazon company, is building capable, safe, and genuinely delightful robots for everyday life. Our goal is simple: make robots people actually want to live and interact with in everyday human spaces. We believe that future won’t arrive until building for robotics becomes far more accessible. Today, too much effort is spent reinventing the fundamentals. We’re changing that by developing tightly integrated hardware and software systems that make it faster, safer, and more intuitive to create real-world robotic products.
US, WA, Seattle
Pricing is one of the most consequential decisions Amazon makes — and the science behind it needs to be causally rigorous, not just predictive. The P2 Optimization Science (P2OS) team builds the machine learning systems that power Amazon's pricing decisions at scale: demand lift models, customer lifetime value frameworks, and the experimentation infrastructure that validates whether our pricing changes actually work. We're hiring an Applied Scientist to own causal inference at the intersection of ML and pricing experimentation. This role exists because our team has identified a real gap: the methodological bridge between econometric analysis (owned by our economists) and production-scale ML pipelines (owned by our engineers) needs a practitioner who lives in both worlds. You'll build CATE estimation models, design analysis workflows for pricing weblabs, and develop the reusable causal ML infrastructure that the broader team — including non-ML scientists — can rely on. This is not a research role. The bias here is toward shipping production-quality causal pipelines with real downstream business impact. You'll measure success by what changes in LTV estimates, what pricing errors your models help avoid, and whether the economists on your team can actually use what you build. If you're a scientist who wants to work on hard causal identification problems in a high-stakes production environment — and who finds satisfaction in making rigorous methods accessible to a broader team — this role is for you. Key job responsibilities * Build causal ML pipelines for pricing — Design, train, evaluate, and deploy end-to-end causal estimation models for pricing use cases. * Own the science on heterogeneous treatment effects — Be the team SME on causal ML methodology: identification strategies, model selection, evaluation standards, and the tradeoffs between econometric and ML approaches to causal estimation. * Support pricing experiment analysis — Contribute causal analysis methodology to pricing weblab and A/B test post-analysis; build reusable tooling that economists can use without requiring ML expertise * Connect model outputs to business outcomes — Define, before writing code, what business metric each model moves; deliver model evaluation reports framed around pricing errors avoided and LTV estimate changes. * Evaluate and adopt novel techniques — Assess applicability of emerging causal inference methods (synthetic DiD, generalized random forests, causal representation learning) to Amazon's pricing context; write internal methodology proposals for adoption * Write internal documentation and methodology papers — Produce at least one internal write-up per half that connects a causal ML technique to a concrete pricing use case; make pipelines extensible and well-documented so other scientists can build on them. * Collaborate across disciplines — Partner closely with the Sr. Economist on identification strategy and causal assumptions; work with SDE and DE partners on production deployment; align with PMs on experiment design requirements A day in the life As an Applied Scientist on the P2OS team, your work directly shapes the prices customers see on hundreds of millions of Amazon products. In a given workweek, you might: * Investigate an optimization anomaly in simulation and trace it back to a model input gap or an unmodeled market dynamic * Design an offline evaluation framework to benchmark competing optimization approaches before committing to online testing * Collaborate with Sr. Economists on the identification strategy for the model you're building for a pricing lab * Present a science proposal for incorporating a new competitiveness or inventory signal into an optimization system * Work cross-team with the experimentation platform team on randomization design. * Develop and write up a novel scientific finding — preparing a paper or technical report for submission to a top-tier venue such as KDD, NeurIPS, or the ACM Conference on Economics and Computation
PL, Gdansk
Have you ever wondered how we give voice to devices — even when they're offline? The Text-to-Speech on Device team at Amazon builds AI-powered voice models that run locally on hardware with limited resources, serving customers across Alexa, automotive, and accessibility experiences for visually impaired users. We sit at the intersection of speech generation, generative AI, and on-device machine learning, and we're looking for a curious, collaborative Applied Scientist to help us push what's possible. In this role, you will research and develop production-ready speech generation models optimized for constrained environments. You will work across the full model lifecycle — from early experimentation and prototyping through to integration on real devices. If you're excited about solving hard scientific problems that directly improve how millions of people interact with technology, we'd love to hear from you. Key job responsibilities - Design and develop end-to-end machine learning models for on-device speech generation, from early research and experimentation through production-ready deployment. - Research and apply advanced techniques in generative AI, model compression, and knowledge distillation to deliver high-quality voice models within tight hardware constraints. - Propose and validate novel scientific approaches by authoring detailed technical specifications and contributing to peer-reviewed publications when appropriate. - Evaluate model performance rigorously, identify improvement opportunities, and iterate on training and inference pipelines to optimize quality and efficiency. - Collaborate with science and engineering teams across cloud and device platforms to bring speech generation capabilities from research prototypes to integrated product experiences. About the team The Text-to-Speech on Device team builds low-footprint AI models for speech generation that run locally on devices such as Android and FireOS platforms. Our models require significantly less computation than cloud-hosted alternatives, enabling offline voice experiences for Alexa, automotive partners, and accessibility solutions. We work closely with device engineering teams and cloud-based speech science teams to deliver the best possible experience for our customers. Our focus in the coming years is expanding the range of voices and languages we support while continuing to improve naturalness and efficiency on constrained hardware.
US, CA, Sunnyvale
Amazon is on a mission to redefine the future of automation — and we're looking for exceptional talent to help lead the way. We are building the next generation of advanced robotic systems that seamlessly blend cutting-edge AI, sophisticated control systems, and novel mechanical design to create adaptable, intelligent automation solutions capable of operating safely alongside humans in dynamic, real-world environments. At Amazon, we leverage the power of machine learning, artificial intelligence, and advanced robotics to solve some of the most complex operational challenges at a scale unlike anywhere else in the world. Our fleet of robots spans hundreds of facilities globally, working in sophisticated coordination to deliver on our promise of customer excellence — and we're just getting started. As a Sr. Scientist in Robot Navigation, you will be at the forefront of this transformation — architecting and delivering navigation systems that are intelligent, safe, and scalable. You will bring deep expertise in learning-based planning and control, a strong understanding of foundation models and their application to embodied agents, and as well as have in-depth understanding of control-theoretic approaches such as model predictive control (MPC)-based trajectory planning. You will develop navigation solutions that seamlessly blend data-driven intelligence with principled control-theoretic guarantees. Our vision is bold: to build navigation systems that allow robots to move fluidly and safely through dynamic environments — understanding context, anticipating change, and adapting in real time. You will lead research that bridges the gap between cutting-edge academic advances and production grade deployment, collaborating with world-class teams pushing the boundaries of robotic autonomy, manipulation, and human-robot interaction. Join us in building the next generation of intelligent navigation systems that will define the future of autonomous robotics at scale. Key job responsibilities - Design, develop, and deploy perception algorithms for robotics systems, including object detection, segmentation, tracking, depth estimation, and scene understanding - Lead research initiatives in computer vision, sensor fusion and 3D perception - Collaborate with cross-functional teams including robotics engineers, software engineers, and product managers to define and deliver perception capabilities - Drive end-to-end ownership of ML models — from data collection and labeling strategy to training, evaluation, and deployment - Mentor junior scientists and engineers; contribute to a culture of technical excellence - Define and track key metrics to measure perception system performance in real-world environments - Publish research findings in top-tier venues (CVPR, ICCV, ECCV, ICRA, NeurIPS, etc.) and contribute to patents A day in the life - Train ML models for deployment in simulation and real-world robots, identify and document their limitations post-deployment - Drive technical discussions within your team and with key stakeholders to develop innovative solutions to address identified limitations - Actively contribute to brainstorming sessions on adjacent topics, bringing fresh perspectives that help peers grow and succeed — and in doing so, build lasting trust across the team - Mentor team members while maintaining significant hands-on contribution to technical solutions About the team Our team is a group is a diverse group of scientists and engineers passionate about building intelligent machines. We value curiosity, rigor, and a bias for action. We believe in learning from failure and iterating quickly toward solutions that matter.
IN, TS, Hyderabad
Have you ever wondered how Amazon launches and maintains a consistent customer experience across hundreds of countries and languages it serves its customers? Are you passionate about data and mathematics, and hope to impact the experience of millions of customers? Are you obsessed with designing simple algorithmic solutions to very challenging problems? If so, we look forward to hearing from you! At Amazon, we strive to be Earth's most customer-centric company, where both internal and external customers can find and discover anything they want in their own language of preference. Our Translations Services (TS) team plays a pivotal role in expanding the reach of our marketplace worldwide and enables thousands of developers and other stakeholders (Product Managers, Program Managers, Linguists) in developing locale specific solutions. Amazon Translations Services (TS) is seeking an Applied Scientist to be based in our Hyderabad office. As a key member of the Science and Engineering team of TS, this person will be responsible for designing algorithmic solutions based on data and mathematics for translating billions of words annually across 130+ and expanding set of locales. The successful applicant will ensure that there is minimal human touch involved in any language translation and accurate translated text is available to our worldwide customers in a streamlined and optimized manner. With access to vast amounts of data, technology, and a diverse community of talented individuals, you will have the opportunity to make a meaningful impact on the way customers and stakeholders engage with Amazon and our platform worldwide. Together, we will drive innovation, solve complex problems, and shape the future of e-commerce. Key job responsibilities * Apply your expertise in LLM models to design, develop, and implement scalable machine learning solutions that address complex language translation-related challenges in the eCommerce space. * Collaborate with cross-functional teams, including software engineers, data scientists, and product managers, to define project requirements, establish success metrics, and deliver high-quality solutions. * Conduct thorough data analysis to gain insights, identify patterns, and drive actionable recommendations that enhance seller performance and customer experiences across various international marketplaces. * Continuously explore and evaluate state-of-the-art modeling techniques and methodologies to improve the accuracy and efficiency of language translation-related systems. * Communicate complex technical concepts effectively to both technical and non-technical stakeholders, providing clear explanations and guidance on proposed solutions and their potential impact. About the team We are a start-up mindset team. As the long-term technical strategy is still taking shape, there is a lot of opportunity for this fresh Science team to innovate by leveraging Gen AI technoligies to build scalable solutions from scratch. Our Vision: Language will not stand in the way of anyone on earth using Amazon products and services. Our Mission: We are the enablers and guardians of translation for Amazon's customers. We do this by offering hands-off-the-wheel service to all Amazon teams, optimizing translation quality and speed at the lowest cost possible.
IN, KA, Bengaluru
As a member of the CMT team, you'll play a key role in the evolution of our Competitive Monitoring systems to solve significantly complex and interesting technical challenges in machine learning, large language models in production, and recommender systems to name a few. The team's work directly impacts customer experience at a worldwide scale. Key job responsibilities Thought leader on the team and help set team directions Research multiple problem domains, suggest various approaches to try and be as hands-on as needed while providing more junior scientists with critical mentorship Collaborate with engineers to come up with the right LLD and HLD to solve key business problems Strong emphasis on communication via writing, internal and external talks, and being able to align with multiple stakeholders A day in the life As an Applied scientist II, a typical day will involve aligning with key product, engineering and business stakeholders ; advising junior scientists on the work they are doing ; reading current research papers and staying up-to-date on AI research ; diving deep as needed to improve CMT models and addressing stakeholders from the science perspective ; writing python code
IN, KA, Bengaluru
As a member of the CMT team, you'll play a key role in the evolution of our Competitive Monitoring systems to solve significantly complex and interesting technical challenges in machine learning, large language models in production, and recommender systems to name a few. The team's work directly impacts customer experience at a worldwide scale. Key job responsibilities 1. Research the problem domain and come up with various approaches to solve the problem. 2. Be willing to experiment quickly and fail fast. 3. Collaborate with engineers to come up with the right end to end solution to the business problems. 4. Ideate on future roadmap for science in CMT 5. Be willing to roll up your sleeves and learn core topics outside applied science, for example ML engineering A day in the life A typical day might involve (a) working on ideas for improving models around product similarity or price recommendations, (b) working closely with other scientists and our ML engineers to ensure that the best models are in production, (c) writing good maintainable code that can be reused and reproduced, (d) sharing your work across CMT and beyond via technical writings and presentations
US, WA, Seattle
Amazon's Customer Experience and Business Trends (CXBT) organization is hiring a Senior Data Scientist for its Benchmarking, Economics, Analytics and Measurement (BEAM) team. BEAM's mission is to improve customer experience across every Amazon business by turning fragmented internal and external data into decision-grade intelligence for senior leaders. This role anchors our Topline View of Retail initiative: an always-on competitive intelligence capability that fuses third-party transaction panels with Amazon's internal signals to answer how Amazon is winning or losing share of wallet, across segments, categories, and time. You will not just analyze metrics — you will decide which metrics should exist, then build the statistical and machine learning models that generate, forecast, and explain them. Given a new business question and a pile of raw panel and internal data, you are the person who defines what "competitiveness" means, translates it into a defensible, reproducible metric and model, and defends the methodology to skeptical senior stakeholders. We are looking for someone with exceptional business judgment and metric intuition, paired with deep hands-on modeling skills: a strong point of view on what to measure, and the technical ability to build the models that measure it at scale. You should be equally comfortable pressure-testing a model's assumptions and explaining its "so what" to a VP in two sentences. Key job responsibilities - Own the definition, methodology, and evolution of BEAM's competitiveness metrics (e.g., share-of-wallet, segment-level penetration, price/selection/fulfillment competitiveness), including the trade-offs behind each definition. - Design, build, and validate statistical and machine learning models — forecasting, causal inference, segmentation, and anomaly detection — that power those metrics and surface competitive shifts. - Pull together disparate third-party transaction-panel data and internal Amazon signals into coherent, reproducible modeling pipelines that leadership can trust for recurring reporting. - Translate ambiguous business questions ("are we losing ground in grocery?") into the right metric, the right model, the right cut of data, and a clear, defensible answer. - Productionize models and analyses so they run reliably as recurring mechanisms, partnering with data engineers to scale and automate. - Produce monthly flash reports and deep-dive narratives that turn models and metrics into decisions for CXBT and business-line leadership. - Set the standard for analytical and modeling rigor on the team — methodology reviews, documentation, and guardrails against misleading cuts. - Mentor and technically uplift other scientists and BIEs on the team — reviewing modeling approaches and raising the bar on rigor (e.g., partnering with our Data Scientist on the MatchIQ model to strengthen methodology and validation). About the team CXBT is a group of diverse functions dedicated to understanding, improving, and influencing customer experience globally, across all of Amazon. We are builders who develop products, services, and data-driven approaches that shape offerings for nearly every Amazon business and customer type — consumers, developers, sellers, brands, employees, investors, streamers, and gamers. We work backwards from customer needs, combine technical and non-technical methods, and track industry and business trends. Our roles span Product Managers, Data Scientists, Economists, Business Intelligence Engineers, Data Engine, Applied Scentist, and Product Manager.
US, MA, N.reading
Amazon is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at an unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic dexterous manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. At Amazon we leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at an unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. Key job responsibilities - Design and implement whole body control methods for balance, locomotion, and dexterous manipulation - Utilize state-of-the-art in methods in learned and model-based control - Create robust and safe behaviors for different terrains and tasks - Implement real-time controllers with stability guarantees - Collaborate effectively with multi-disciplinary teams to co-design hardware and algorithms for loco-manipulation - Mentor junior engineer and scientists